/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index 1.22.4 1.4.1 1.22.4 1.4.1 Gene: embB Drug: ethambutol aaindex_df contains non-numerical data Total no. of non-numerial columns: 2 Selecting numerical data only PASS: successfully selected numerical columns only for aaindex_df Now checking for NA in the remaining aaindex_cols Counting aaindex_df cols with NA ncols with NA: 4 columns Dropping these... Original ncols: 127 Revised df ncols: 123 Checking NA in revised df... PASS: cols with NA successfully dropped from aaindex_df Proceeding with combining aa_df with other features_df PASS: ncols match Expected ncols: 123 Got: 123 Total no. of columns in clean aa_df: 123 Proceeding to merge, expected nrows in merged_df: 858 PASS: my_features_df and aa_df successfully combined nrows: 858 ncols: 271 count of NULL values before imputation or_mychisq 244 log10_or_mychisq 244 dtype: int64 count of NULL values AFTER imputation mutationinformation 0 or_rawI 0 logorI 0 dtype: int64 PASS: OR values imputed, data ready for ML Total no. of features for aaindex: 123 Genomic features being used EXCLUDING odds ratio (n): 5 These are: ['maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'] dst column exists and this is identical to drug column: ethambutol All feature names: ['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', 'electro_sm', 'electro_ss', 'disulfide_rr', 'disulfide_mm', 'disulfide_sm', 'disulfide_ss', 'hbonds_rr', 'hbonds_mm', 'hbonds_sm', 'hbonds_ss', 'partcov_rr', 'partcov_mm', 'partcov_sm', 'partcov_ss', 'vdwclashes_rr', 'vdwclashes_mm', 'vdwclashes_sm', 'vdwclashes_ss', 'volumetric_rr', 'volumetric_mm', 'volumetric_ss', 'ligand_distance', 'ligand_affinity_change', 'mmcsm_lig', 'mcsm_ppi2_affinity', 'interface_dist', 'ALTS910101', 'AZAE970101', 'AZAE970102', 'BASU010101', 'BENS940101', 'BENS940102', 'BENS940103', 'BENS940104', 'BETM990101', 'BLAJ010101', 'BONM030101', 'BONM030102', 'BONM030103', 'BONM030104', 'BONM030105', 'BONM030106', 'BRYS930101', 'CROG050101', 'CSEM940101', 'DAYM780301', 'DAYM780302', 'DOSZ010101', 'DOSZ010102', 'DOSZ010103', 'DOSZ010104', 'FEND850101', 'FITW660101', 'GEOD900101', 'GIAG010101', 'GONG920101', 'GRAR740104', 'HENS920101', 'HENS920102', 'HENS920103', 'HENS920104', 'JOHM930101', 'JOND920103', 'JOND940101', 'KANM000101', 'KAPO950101', 'KESO980101', 'KESO980102', 'KOLA920101', 'KOLA930101', 'KOSJ950100_RSA_SST', 'KOSJ950100_SST', 'KOSJ950110_RSA', 'KOSJ950115', 'LEVJ860101', 'LINK010101', 'LIWA970101', 'LUTR910101', 'LUTR910102', 'LUTR910103', 'LUTR910104', 'LUTR910105', 'LUTR910106', 'LUTR910107', 'LUTR910108', 'LUTR910109', 'MCLA710101', 'MCLA720101', 'MEHP950102', 'MICC010101', 'MIRL960101', 'MIYS850102', 'MIYS850103', 'MIYS930101', 'MIYS960101', 'MIYS960102', 'MIYS960103', 'MIYS990106', 'MIYS990107', 'MIYT790101', 'MOHR870101', 'MOOG990101', 'MUET010101', 'MUET020101', 'MUET020102', 'NAOD960101', 'NGPC000101', 'NIEK910101', 'NIEK910102', 'OGAK980101', 'OVEJ920100_RSA', 'OVEJ920101', 'OVEJ920102', 'OVEJ920103', 'PRLA000101', 'PRLA000102', 'QUIB020101', 'QU_C930101', 'QU_C930102', 'QU_C930103', 'RIER950101', 'RISJ880101', 'RUSR970101', 'RUSR970102', 'RUSR970103', 'SIMK990101', 'SIMK990102', 'SIMK990103', 'SIMK990104', 'SIMK990105', 'SKOJ000101', 'SKOJ000102', 'SKOJ970101', 'TANS760101', 'TANS760102', 'THOP960101', 'TOBD000101', 'TOBD000102', 'TUDE900101', 'VENM980101', 'VOGG950101', 'WEIL970101', 'WEIL970102', 'ZHAC000101', 'ZHAC000102', 'ZHAC000103', 'ZHAC000104', 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique', 'dst', 'dst_mode'] PASS: but NOT writing mask file PASS: But NOT writing processed file ################################################################# SUCCESS: Extacted training data for gene: embb Dim of training_df: (858, 175) This EXCLUDES Odds Ratio ############################################################ Input params: Dim of input df: (858, 175) Data type to split: actual Split type: 70_30 target colname: dst_mode oversampling enabled PASS: x_features has no target variable and no dst column Dropped cols: 2 These were: dst_mode and dst No. of cols in input df: 175 No.of cols dropped: 2 No. of columns for x_features: 173 ------------------------------------------------------------- Successfully generated training and test data: Data used: actual Split type: 70_30 Total no. of input features: 173 --------No. of numerical features: 167 --------No. of categorical features: 6 =========================== Resampling: NONE Baseline =========================== Total data size: 315 Train data size: (211, 173) y_train numbers: Counter({0: 169, 1: 42}) Test data size: (104, 173) y_test_numbers: Counter({0: 84, 1: 20}) y_train ratio: 4.023809523809524 y_test ratio: 4.2 ------------------------------------------------------------- Simple Random OverSampling Counter({0: 169, 1: 169}) (338, 173) Simple Random UnderSampling Counter({0: 42, 1: 42}) (84, 173) Simple Combined Over and UnderSampling Counter({0: 169, 1: 169}) (338, 173) SMOTE_NC OverSampling Counter({0: 169, 1: 169}) (338, 173) Generated Resampled data as below: ================================= Resampling: Random oversampling ================================ Train data size: (338, 173) y_train numbers: 338 y_train ratio: 1.0 y_test ratio: 4.2 ================================ Resampling: Random underampling ================================ Train data size: (84, 173) y_train numbers: 84 y_train ratio: 1.0 y_test ratio: 4.2 ================================ Resampling:Combined (over+under) ================================ Train data size: (338, 173) y_train numbers: 338 y_train ratio: 1.0 y_test ratio: 4.2 ============================== Resampling: Smote NC ============================== Train data size: (338, 173) y_train numbers: 338 y_train ratio: 1.0 y_test ratio: 4.2 ------------------------------------------------------------- ============================================================== Running several classification models (n): 24 List of models: ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('Gaussian NB', GaussianNB()) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('K-Nearest Neighbors', KNeighborsClassifier()) ('LDA', LinearDiscriminantAnalysis()) ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(cv=3, random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Multinomial', MultinomialNB()) ('Naive Bayes', BernoulliNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=12, random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Random Forest', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42)) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=3)) ('SVC', SVC(random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=12, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ================================================================ Running classifier: 1 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.11087966 0.11165428 0.11089563 0.11232376 0.11184454 0.11282921 0.10945964 0.1110239 0.11263824 0.11478257] mean value: 0.11183314323425293 key: score_time value: [0.0157249 0.0150516 0.01565909 0.0153954 0.01533413 0.015172 0.01508856 0.01588202 0.01541209 0.01552081] mean value: 0.015424060821533202 key: test_mcc value: [ 0.2057983 0.66885605 0.14852213 0.14852213 0.14852213 0.46097722 -0.23529412 0.68599434 0.14852213 0.29827938] mean value: 0.2678699707305227 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.28571429 0.66666667 0.28571429 0.28571429 0.28571429 0.4 0. 0.72727273 0.28571429 0.44444444] mean value: 0.3666955266955267 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.5 1. 0.33333333 0.33333333 0.33333333 1. 0. 0.57142857 0.33333333 0.5 ] mean value: 0.4904761904761904 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.2 0.5 0.25 0.25 0.25 0.25 0. 1. 0.25 0.4 ] mean value: 0.335 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.77272727 0.9047619 0.76190476 0.76190476 0.76190476 0.85714286 0.61904762 0.85714286 0.76190476 0.76190476] mean value: 0.7820346320346321 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.57058824 0.75 0.56617647 0.56617647 0.56617647 0.625 0.38235294 0.91176471 0.56617647 0.6375 ] mean value: 0.6141911764705883 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.16666667 0.5 0.16666667 0.16666667 0.16666667 0.25 0. 0.57142857 0.16666667 0.28571429] mean value: 0.244047619047619 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. 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Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.6s remaining: 1.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.6s remaining: 1.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.6s remaining: 0.2s Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... 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[Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.6s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.6s remaining: 1.2s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.6s remaining: 1.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.6s remaining: 0.2s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.6s remaining: 1.2s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.6s remaining: 1.2s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.6s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.6s remaining: 1.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.6s remaining: 0.2s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.6s remaining: 1.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.6s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.6s remaining: 0.2s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.6s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.7s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.7s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.7s remaining: 0.2s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.7s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.7s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.7s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.7s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.7s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. 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[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished MCC on Blind test: -0.06 MCC on Training: 0.27 Running classifier: 2 Model_name: Bagging Classifier Model func: BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3))]) key: fit_time value: [0.37595916 0.16202974 0.16151166 0.16338706 0.19249821 0.19518161 0.19622564 0.16139412 0.18356013 0.16994071] mean value: 0.19616880416870117 key: score_time value: [0.05256581 0.04658723 0.03797793 0.04572201 0.07931066 0.08090377 0.04033399 0.05165029 0.04718423 0.05154824] mean value: 0.05337841510772705 key: test_mcc value: [-0.11834527 -0.1573779 0.25573908 -0.10846523 -0.1573779 0. 0.46097722 0.46097722 -0.10846523 -0.18136906] mean value: 0.03462929476479944 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0. 0. 0.33333333 0. 0. 0. 0.4 0.4 0. 0. ] mean value: 0.11333333333333333 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0. 0. 0.5 0. 0. 0. 1. 1. 0. 0. ] mean value: 0.25 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0. 0. 0.25 0. 0. 0. 0.25 0.25 0. 0. ] mean value: 0.075 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.72727273 0.71428571 0.80952381 0.76190476 0.71428571 0.80952381 0.85714286 0.85714286 0.76190476 0.66666667] mean value: 0.767965367965368 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.47058824 0.44117647 0.59558824 0.47058824 0.44117647 0.5 0.625 0.625 0.47058824 0.4375 ] mean value: 0.5077205882352942 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0. 0. 0.2 0. 0. 0. 0.25 0.25 0. 0. ] mean value: 0.06999999999999999 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.06 MCC on Training: 0.03 Running classifier: 3 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.01399255 0.01461363 0.01568103 0.01495552 0.01588368 0.01579928 0.01490092 0.01501775 0.01519322 0.01593924] mean value: 0.015197682380676269 key: score_time value: [0.00874734 0.00863719 0.00868559 0.00859714 0.00860095 0.00939798 0.00923228 0.00854039 0.00872231 0.00898528] mean value: 0.008814644813537598 key: test_mcc value: [ 0.22352941 0.46097722 0.31506302 -0.19802951 -0.03834825 -0.1573779 -0.10846523 -0.17503501 0.38235294 -0.05 ] mean value: 0.06546667021886635 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.4 0.4 0.46153846 0. 0.2 0. 0. 0.15384615 0.5 0.2 ] mean value: 0.23153846153846155 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.4 1. 0.33333333 0. 0.16666667 0. 0. 0.11111111 0.5 0.2 ] mean value: 0.27111111111111114 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.4 0.25 0.75 0. 0.25 0. 0. 0.25 0.5 0.2 ] mean value: 0.26 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.72727273 0.85714286 0.66666667 0.66666667 0.61904762 0.71428571 0.76190476 0.47619048 0.80952381 0.61904762] mean value: 0.6917748917748918 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.61176471 0.625 0.69852941 0.41176471 0.47794118 0.44117647 0.47058824 0.38970588 0.69117647 0.475 ] mean value: 0.529264705882353 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.25 0.25 0.3 0. 0.11111111 0. 0. 0.08333333 0.33333333 0.11111111] mean value: 0.1438888888888889 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.01 MCC on Training: 0.07 Running classifier: 4 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.00877571 0.00974083 0.00972581 0.00930309 0.00904155 0.00859761 0.0089488 0.0092833 0.00929785 0.00878477] mean value: 0.009149932861328125 key: score_time value: [0.00855303 0.00914764 0.00867605 0.00888634 0.00833988 0.00851965 0.00859547 0.00855422 0.00867844 0.00875187] mean value: 0.00867025852203369 key: test_mcc value: [ 0.10056599 -0.10846523 -0.08574929 0.2300895 0.01355815 0.46097722 -0.19802951 0.01355815 -0.19802951 0.38890873] mean value: 0.06173842114423467 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.25 0. 0.18181818 0.4 0.22222222 0.4 0. 0.22222222 0. 0.54545455] mean value: 0.22217171717171716 key: train_fscore value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.33333333 0. 0.14285714 0.33333333 0.2 1. 0. 0.2 0. 0.5 ] mean value: 0.27095238095238094 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.2 0. 0.25 0.5 0.25 0.25 0. 0.25 0. 0.6 ] mean value: 0.22999999999999998 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.72727273 0.76190476 0.57142857 0.71428571 0.66666667 0.85714286 0.66666667 0.66666667 0.66666667 0.76190476] mean value: 0.706060606060606 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.54117647 0.47058824 0.44852941 0.63235294 0.50735294 0.625 0.41176471 0.50735294 0.41176471 0.70625 ] mean value: 0.5262132352941177 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.14285714 0. 0.1 0.25 0.125 0.25 0. 0.125 0. 0.375 ] mean value: 0.1367857142857143 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.05 MCC on Training: 0.06 Running classifier: 5 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.09814739 0.09416938 0.09438515 0.09332323 0.09386039 0.09310055 0.09294868 0.09234643 0.09381104 0.09379029] mean value: 0.09398825168609619 key: score_time value: [0.0170064 0.01693916 0.01699233 0.01715183 0.01709747 0.01708889 0.01762915 0.01705933 0.01738858 0.0170989 ] mean value: 0.017145204544067382 key: test_mcc value: [-0.11834527 -0.10846523 0.07352941 0. 0.25573908 0. -0.19802951 0.14852213 -0.19802951 -0.18136906] mean value: -0.03264479530055755 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0. 0. 0.25 0. 0.33333333 0. 0. 0.28571429 0. 0. ] mean value: 0.0869047619047619 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0. 0. 0.25 0. 0.5 0. 0. 0.33333333 0. 0. ] mean value: 0.10833333333333332 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0. 0. 0.25 0. 0.25 0. 0. 0.25 0. 0. ] mean value: 0.075 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.72727273 0.76190476 0.71428571 0.80952381 0.80952381 0.80952381 0.66666667 0.76190476 0.66666667 0.66666667] mean value: 0.7393939393939395 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.47058824 0.47058824 0.53676471 0.5 0.59558824 0.5 0.41176471 0.56617647 0.41176471 0.4375 ] mean value: 0.49007352941176474 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0. 0. 0.14285714 0. 0.2 0. 0. 0.16666667 0. 0. ] mean value: 0.05095238095238095 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.01 MCC on Training: -0.03 Running classifier: 6 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.33203292 0.32670379 0.33629227 0.33217692 0.3322134 0.33706427 0.33463764 0.33234072 0.33780146 0.33835626] mean value: 0.33396196365356445 key: score_time value: [0.00895023 0.00891209 0.00913334 0.00941348 0.00919199 0.00904441 0.00909615 0.00892019 0.00905156 0.00913048] mean value: 0.009084391593933105 key: test_mcc value: [ 0.10056599 -0.10846523 -0.10846523 -0.1573779 -0.1573779 0.25573908 -0.10846523 0.25573908 0.14852213 0.09128709] mean value: 0.021170190019201323 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.25 0. 0. 0. 0. 0.33333333 0. 0.33333333 0.28571429 0.25 ] mean value: 0.14523809523809522 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.33333333 0. 0. 0. 0. 0.5 0. 0.5 0.33333333 0.33333333] mean value: 0.19999999999999998 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.2 0. 0. 0. 0. 0.25 0. 0.25 0.25 0.2 ] mean value: 0.11499999999999999 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.72727273 0.76190476 0.76190476 0.71428571 0.71428571 0.80952381 0.76190476 0.80952381 0.76190476 0.71428571] mean value: 0.7536796536796537 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.54117647 0.47058824 0.47058824 0.44117647 0.44117647 0.59558824 0.47058824 0.59558824 0.56617647 0.5375 ] mean value: 0.5130147058823529 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.14285714 0. 0. 0. 0. 0.2 0. 0.2 0.16666667 0.14285714] mean value: 0.08523809523809525 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.13 MCC on Training: 0.02 Running classifier: 7 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.00858212 0.00869179 0.00891566 0.00948167 0.00953007 0.00962377 0.00849533 0.00900435 0.00893855 0.00960946] mean value: 0.009087276458740235 key: score_time value: [0.00850439 0.00842214 0.00920916 0.00882483 0.00917149 0.00912738 0.00849748 0.00919485 0.00836229 0.00866723] mean value: 0.00879812240600586 key: test_mcc value: [-0.08856149 0.02312486 0.58300061 -0.23529412 0.2300895 0.38235294 -0.21968621 0.11891287 -0.3805212 0.31622777] mean value: 0.07296455329723937 key: train_mcc value: [0.35139205 0.4097246 0.51953528 0.38316294 0.46426557 0.49322003 0.36716674 0.46585125 0.43070552 0.4731987 ] mean value: 0.43582226879613206 key: test_fscore value: [0.18181818 0.28571429 0.66666667 0. 0.4 0.5 0.14285714 0.33333333 0. 0.5 ] mean value: 0.30103896103896105 key: train_fscore value: [0.4950495 0.54 0.61538462 0.51785714 0.57692308 0.60215054 0.50420168 0.58 0.54867257 0.58426966] mean value: 0.5564508787715038 key: test_precision value: [0.16666667 0.2 0.6 0. 0.33333333 0.5 0.1 0.25 0. 0.42857143] mean value: 0.25785714285714284 key: train_precision value: [0.390625 0.43548387 0.48484848 0.39189189 0.45454545 0.50909091 0.37037037 0.46774194 0.41333333 0.5 ] mean value: 0.44179312505320567 key: test_recall value: [0.2 0.5 0.75 0. 0.5 0.5 0.25 0.5 0. 0.6 ] mean value: 0.38 key: train_recall value: [0.67567568 0.71052632 0.84210526 0.76315789 0.78947368 0.73684211 0.78947368 0.76315789 0.81578947 0.7027027 ] mean value: 0.7588904694167853 key: test_accuracy value: [0.59090909 0.52380952 0.85714286 0.61904762 0.71428571 0.80952381 0.42857143 0.61904762 0.42857143 0.71428571] mean value: 0.6305194805194805 key: train_accuracy value: [0.73015873 0.75789474 0.78947368 0.71578947 0.76842105 0.80526316 0.68947368 0.77894737 0.73157895 0.80526316] mean value: 0.7572263993316624 key: test_roc_auc value: [0.45294118 0.51470588 0.81617647 0.38235294 0.63235294 0.69117647 0.36029412 0.57352941 0.26470588 0.675 ] mean value: 0.5363235294117648 key: train_roc_auc value: [0.70954836 0.74013158 0.80921053 0.73355263 0.77631579 0.77960526 0.72697368 0.77302632 0.76315789 0.76638403] mean value: 0.7577906079454066 key: test_jcc value: [0.1 0.16666667 0.5 0. 0.25 0.33333333 0.07692308 0.2 0. 0.33333333] mean value: 0.19602564102564102 key: train_jcc value: [0.32894737 0.36986301 0.44444444 0.34939759 0.40540541 0.43076923 0.33707865 0.4084507 0.37804878 0.41269841] mean value: 0.3865103602197172 MCC on Blind test: 0.04 MCC on Training: 0.07 Running classifier: 8 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.05350971 0.05497694 0.07207608 0.05092645 0.08255339 0.06044888 0.06847024 0.06970692 0.07243848 0.05968118] mean value: 0.06447882652282715 key: score_time value: [0.03527999 0.02880836 0.02151418 0.02248025 0.02465463 0.02109933 0.01687336 0.02199769 0.02152061 0.02369976] mean value: 0.02379281520843506 key: test_mcc value: [-0.11834527 -0.10846523 0.25573908 0. -0.10846523 -0.10846523 -0.1573779 -0.10846523 -0.1573779 -0.125 ] mean value: -0.07362228933724477 key: train_mcc value: [0.89778684 0.90007032 0.90007032 0.88310487 0.93369956 0.91693202 0.90007032 0.91693202 0.93369956 0.84540802] mean value: 0.9027773856638804 key: test_fscore value: [0. 0. 0.33333333 0. 0. 0. 0. 0. 0. 0. ] mean value: 0.03333333333333333 key: train_fscore value: [0.91176471 0.91428571 0.91428571 0.89855072 0.94444444 0.92957746 0.91428571 0.92957746 0.94444444 0.86153846] mean value: 0.9162754853381992 key: test_precision value: [0. 0. 0.5 0. 0. 0. 0. 0. 0. 0. ] mean value: 0.05 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0. 0. 0.25 0. 0. 0. 0. 0. 0. 0. ] mean value: 0.025 key: train_recall value: [0.83783784 0.84210526 0.84210526 0.81578947 0.89473684 0.86842105 0.84210526 0.86842105 0.89473684 0.75675676] mean value: 0.8463015647226173 key: test_accuracy value: [0.72727273 0.76190476 0.80952381 0.80952381 0.76190476 0.76190476 0.71428571 0.76190476 0.71428571 0.71428571] mean value: 0.7536796536796537 key: train_accuracy value: [0.96825397 0.96842105 0.96842105 0.96315789 0.97894737 0.97368421 0.96842105 0.97368421 0.97894737 0.95263158] mean value: 0.9694569757727652 key: test_roc_auc value: [0.47058824 0.47058824 0.59558824 0.5 0.47058824 0.47058824 0.44117647 0.47058824 0.44117647 0.46875 ] mean value: 0.47996323529411766 key: train_roc_auc value: [0.91891892 0.92105263 0.92105263 0.90789474 0.94736842 0.93421053 0.92105263 0.93421053 0.94736842 0.87837838] mean value: 0.9231507823613088 key: test_jcc value: [0. 0. 0.2 0. 0. 0. 0. 0. 0. 0. ] mean value: 0.02 key: train_jcc value: [0.83783784 0.84210526 0.84210526 0.81578947 0.89473684 0.86842105 0.84210526 0.86842105 0.89473684 0.75675676] mean value: 0.8463015647226173 MCC on Blind test: 0.03 MCC on Training: -0.07 Running classifier: 9 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.01885009 0.01273751 0.01001811 0.00908303 0.00891805 0.0094769 0.00959611 0.00967026 0.00933075 0.01006651] mean value: 0.010774731636047363 key: score_time value: [0.05625081 0.0111382 0.01115131 0.010427 0.01032829 0.01128149 0.01085448 0.0152669 0.0106616 0.01596332] mean value: 0.016332340240478516 key: test_mcc value: [-0.11834527 0.25573908 -0.10846523 0. 0. 0. -0.10846523 -0.19802951 -0.1573779 -0.18136906] mean value: -0.06163131116037025 key: train_mcc value: [0.13883382 0.1816096 0.21409215 0.22276053 0.16439899 0.21067524 0.11175975 0.29462783 0.26015295 0.16824543] mean value: 0.1967156285700589 key: test_fscore value: [0. 0.33333333 0. 0. 0. 0. 0. 0. 0. 0. ] mean value: 0.03333333333333333 key: train_fscore value: [0.13953488 0.17777778 0.24489796 0.2173913 0.13953488 0.18181818 0.13333333 0.29166667 0.25531915 0.14285714] mean value: 0.19241312823626325 key: test_precision value: [0. 0.5 0. 0. 0. 0. 0. 0. 0. 0. ] mean value: 0.05 key: train_precision value: [0.5 0.57142857 0.54545455 0.625 0.6 0.66666667 0.42857143 0.7 0.66666667 0.6 ] mean value: 0.5903787878787878 key: test_recall value: [0. 0.25 0. 0. 0. 0. 0. 0. 0. 0. ] mean value: 0.025 key: train_recall value: [0.08108108 0.10526316 0.15789474 0.13157895 0.07894737 0.10526316 0.07894737 0.18421053 0.15789474 0.08108108] mean value: 0.11621621621621622 key: test_accuracy value: [0.72727273 0.80952381 0.76190476 0.80952381 0.80952381 0.80952381 0.76190476 0.66666667 0.71428571 0.66666667] mean value: 0.7536796536796537 key: train_accuracy value: [0.8042328 0.80526316 0.80526316 0.81052632 0.80526316 0.81052632 0.79473684 0.82105263 0.81578947 0.81052632] mean value: 0.8083180172653857 key: test_roc_auc value: [0.47058824 0.59558824 0.47058824 0.5 0.5 0.5 0.47058824 0.41176471 0.44117647 0.4375 ] mean value: 0.4797794117647059 key: train_roc_auc value: [0.53067212 0.54276316 0.5625 0.55592105 0.53289474 0.54605263 0.52631579 0.58223684 0.56907895 0.53400459] mean value: 0.5482439870210768 key: test_jcc value: [0. 0.2 0. 0. 0. 0. 0. 0. 0. 0. ] mean value: 0.02 key: train_jcc value: [0.075 0.09756098 0.13953488 0.12195122 0.075 0.1 0.07142857 0.17073171 0.14634146 0.07692308] mean value: 0.10744718979262372 MCC on Blind test: 0.16 MCC on Training: -0.06 Running classifier: 10 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.02968645 0.02700758 0.03561831 0.02751684 0.02813363 0.02697134 0.02723908 0.02675843 0.02752042 0.05743456] mean value: 0.03138866424560547 key: score_time value: [0.01171422 0.01196003 0.01175952 0.01192784 0.0119822 0.01195025 0.01209164 0.01186514 0.0212791 0.02813983] mean value: 0.014466977119445801 key: test_mcc value: [ 0.22352941 0.2300895 -0.23529412 0.07352941 0.2300895 0.25573908 -0.03834825 0.070014 -0.08574929 0.2125 ] mean value: 0.09360992408720588 key: train_mcc value: [0.96759228 0.91868863 0.96710526 0.90131579 0.93597098 0.92118622 0.93597098 0.93859064 0.96824584 0.89931108] mean value: 0.9353977678783185 key: test_fscore value: [0.4 0.4 0. 0.25 0.4 0.33333333 0.2 0.30769231 0.18181818 0.4 ] mean value: 0.28728438228438224 key: train_fscore value: [0.97368421 0.93506494 0.97368421 0.92105263 0.94871795 0.93670886 0.94871795 0.95 0.97435897 0.91891892] mean value: 0.9480908639169799 key: test_precision value: [0.4 0.33333333 0. 0.25 0.33333333 0.5 0.16666667 0.22222222 0.14285714 0.4 ] mean value: 0.27484126984126983 key: train_precision value: [0.94871795 0.92307692 0.97368421 0.92105263 0.925 0.90243902 0.925 0.9047619 0.95 0.91891892] mean value: 0.9292651561971204 key: test_recall value: [0.4 0.5 0. 0.25 0.5 0.25 0.25 0.5 0.25 0.4 ] mean value: 0.32999999999999996 key: train_recall value: [1. 0.94736842 0.97368421 0.92105263 0.97368421 0.97368421 0.97368421 1. 1. 0.91891892] mean value: 0.9682076813655762 key: test_accuracy value: [0.72727273 0.71428571 0.61904762 0.71428571 0.71428571 0.80952381 0.61904762 0.57142857 0.57142857 0.71428571] mean value: 0.6774891774891775 key: train_accuracy value: [0.98941799 0.97368421 0.98947368 0.96842105 0.97894737 0.97368421 0.97894737 0.97894737 0.98947368 0.96842105] mean value: 0.978941798941799 key: test_roc_auc value: [0.61176471 0.63235294 0.38235294 0.53676471 0.63235294 0.59558824 0.47794118 0.54411765 0.44852941 0.60625 ] mean value: 0.5468014705882352 key: train_roc_auc value: [0.99342105 0.96381579 0.98355263 0.95065789 0.97697368 0.97368421 0.97697368 0.98684211 0.99342105 0.94965554] mean value: 0.9748997643153989 key: test_jcc value: [0.25 0.25 0. 0.14285714 0.25 0.2 0.11111111 0.18181818 0.1 0.25 ] mean value: 0.17357864357864358 key: train_jcc value: [0.94871795 0.87804878 0.94871795 0.85365854 0.90243902 0.88095238 0.90243902 0.9047619 0.95 0.85 ] mean value: 0.901973554900384 MCC on Blind test: 0.03 MCC on Training: 0.09 Running classifier: 11 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.05547547 0.03744316 0.03718042 0.03747988 0.03709435 0.03632212 0.03679276 0.05526567 0.03945208 0.03526807] mean value: 0.040777397155761716 key: score_time value: [0.01398063 0.01389503 0.01392102 0.01364636 0.0135591 0.01399946 0.01346898 0.01346612 0.01370907 0.01389885] mean value: 0.013754463195800782 key: test_mcc value: [ 0.40237391 0.49507377 0. 0.46097722 0.14852213 0. 0.25573908 0.25573908 -0.1573779 -0.125 ] mean value: 0.1736047297795046 key: train_mcc value: [0.47144643 0.43915503 0.5573704 0.43915503 0.51171618 0.48795004 0.48865052 0.53508772 0.55713599 0.4717794 ] mean value: 0.4959446743679591 key: test_fscore value: [0.33333333 0.57142857 0. 0.4 0.28571429 0. 0.33333333 0.33333333 0. 0. ] mean value: 0.2257142857142857 key: train_fscore value: [0.47058824 0.45283019 0.5862069 0.45283019 0.51851852 0.49056604 0.50909091 0.56140351 0.57142857 0.47058824] mean value: 0.5084051290044227 key: test_precision value: [1. 0.66666667 0. 1. 0.33333333 0. 0.5 0.5 0. 0. ] mean value: 0.4 key: train_precision value: [0.85714286 0.8 0.85 0.8 0.875 0.86666667 0.82352941 0.84210526 0.88888889 0.85714286] mean value: 0.8460475944763871 key: test_recall value: [0.2 0.5 0. 0.25 0.25 0. 0.25 0.25 0. 0. ] mean value: 0.16999999999999998 key: train_recall value: [0.32432432 0.31578947 0.44736842 0.31578947 0.36842105 0.34210526 0.36842105 0.42105263 0.42105263 0.32432432] mean value: 0.3648648648648648 key: test_accuracy value: [0.81818182 0.85714286 0.80952381 0.85714286 0.76190476 0.80952381 0.80952381 0.80952381 0.71428571 0.71428571] mean value: 0.7961038961038962 key: train_accuracy value: [0.85714286 0.84736842 0.87368421 0.84736842 0.86315789 0.85789474 0.85789474 0.86842105 0.87368421 0.85789474] mean value: 0.8604511278195488 key: test_roc_auc value: [0.6 0.72058824 0.5 0.625 0.56617647 0.5 0.59558824 0.59558824 0.44117647 0.46875 ] mean value: 0.5612867647058823 key: train_roc_auc value: [0.65558321 0.64802632 0.71381579 0.64802632 0.67763158 0.66447368 0.67434211 0.70065789 0.70394737 0.65562621] mean value: 0.6742130481875064 key: test_jcc value: [0.2 0.4 0. 0.25 0.16666667 0. 0.2 0.2 0. 0. ] mean value: 0.14166666666666666 key: train_jcc value: [0.30769231 0.29268293 0.41463415 0.29268293 0.35 0.325 0.34146341 0.3902439 0.4 0.30769231] mean value: 0.34220919324577864 MCC on Blind test: 0.01 MCC on Training: 0.17 Running classifier: 12 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(cv=3, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(cv=3, random_state=42))]) key: fit_time value: [0.41487789 0.40305662 0.43004513 0.43030334 0.51490045 0.41327119 0.48737717 0.45903444 0.49840999 0.48150635] mean value: 0.4532782554626465 key: score_time value: [0.01190519 0.0124836 0.01190042 0.01337266 0.01185656 0.01332092 0.01182222 0.01577592 0.01218629 0.01271462] mean value: 0.012733840942382812 key: test_mcc value: [ 0.40237391 0. -0.19802951 0. 0.25573908 0. 0.46097722 0.46097722 -0.10846523 -0.125 ] mean value: 0.11485726957996101 key: train_mcc value: [0.30036113 0. 0.77564667 0.35355339 0.41110521 0.38311227 0.41247896 0.32677223 0.35355339 0.39088013] mean value: 0.3707463381586093 key: test_fscore value: [0.33333333 0. 0. 0. 0.33333333 0. 0.4 0.4 0. 0. ] mean value: 0.14666666666666667 key: train_fscore value: [0.29787234 0. 0.80597015 0.33333333 0.4 0.36734694 0.375 0.32653061 0.33333333 0.34782609] mean value: 0.358721279432286 key: test_precision value: [1. 0. 0. 0. 0.5 0. 1. 1. 0. 0. ] mean value: 0.35 key: train_precision value: [0.7 0. 0.93103448 0.8 0.83333333 0.81818182 0.9 0.72727273 0.8 0.88888889] mean value: 0.7398711250435388 key: test_recall value: [0.2 0. 0. 0. 0.25 0. 0.25 0.25 0. 0. ] mean value: 0.095 key: train_recall value: [0.18918919 0. 0.71052632 0.21052632 0.26315789 0.23684211 0.23684211 0.21052632 0.21052632 0.21621622] mean value: 0.24843527738264579 key: test_accuracy value: [0.81818182 0.80952381 0.66666667 0.80952381 0.80952381 0.80952381 0.85714286 0.85714286 0.76190476 0.71428571] mean value: 0.7913419913419913 key: train_accuracy value: [0.82539683 0.8 0.93157895 0.83157895 0.84210526 0.83684211 0.84210526 0.82631579 0.83157895 0.84210526] mean value: 0.8409607351712616 key: test_roc_auc value: [0.6 0.5 0.41176471 0.5 0.59558824 0.5 0.625 0.625 0.47058824 0.46875 ] mean value: 0.5296691176470588 key: train_roc_auc value: [0.58472617 0.5 0.84868421 0.59868421 0.625 0.61184211 0.61513158 0.59539474 0.59868421 0.60484013] mean value: 0.6182987360425442 key: test_jcc value: [0.2 0. 0. 0. 0.2 0. 0.25 0.25 0. 0. ] mean value: 0.09 key: train_jcc value: [0.175 0. 0.675 0.2 0.25 0.225 0.23076923 0.19512195 0.2 0.21052632] mean value: 0.2361417497778217 MCC on Blind test: -0.07 MCC on Training: 0.11 Running classifier: 13 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [0.84006286 0.73822308 0.73620033 0.94517684 0.83446288 0.78899336 0.86012626 0.78453374 0.80342698 0.83860803] mean value: 0.8169814348220825 key: score_time value: [0.0124495 0.01223612 0.01286173 0.01249576 0.01251912 0.01245856 0.01282978 0.01248574 0.01580977 0.01346183] mean value: 0.01296079158782959 key: test_mcc value: [ 0.22352941 0.49507377 -0.23529412 0.25573908 0.07352941 0.25573908 0.2300895 0.14852213 0.2300895 0.29827938] mean value: 0.19752971406132774 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.4 0.57142857 0. 0.33333333 0.25 0.33333333 0.4 0.28571429 0.4 0.44444444] mean value: 0.3418253968253968 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.4 0.66666667 0. 0.5 0.25 0.5 0.33333333 0.33333333 0.33333333 0.5 ] mean value: 0.3816666666666667 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.4 0.5 0. 0.25 0.25 0.25 0.5 0.25 0.5 0.4 ] mean value: 0.32999999999999996 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.72727273 0.85714286 0.61904762 0.80952381 0.71428571 0.80952381 0.71428571 0.76190476 0.71428571 0.76190476] mean value: 0.748917748917749 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.61176471 0.72058824 0.38235294 0.59558824 0.53676471 0.59558824 0.63235294 0.56617647 0.63235294 0.6375 ] mean value: 0.5911029411764706 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.25 0.4 0. 0.2 0.14285714 0.2 0.25 0.16666667 0.25 0.28571429] mean value: 0.2145238095238095 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.04 MCC on Training: 0.2 Running classifier: 14 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.01269269 0.01262927 0.01042986 0.00922608 0.01034045 0.01027966 0.00998116 0.00947356 0.01028848 0.009763 ] mean value: 0.010510420799255371 key: score_time value: [0.01194263 0.01120448 0.00958633 0.00935197 0.0097096 0.00960612 0.00923586 0.00906849 0.0095017 0.00937009] mean value: 0.009857726097106934 key: test_mcc value: [ 0.40237391 0. 0. 0. 0.25573908 0. 0.14852213 -0.19802951 -0.10846523 0. ] mean value: 0.050014038151682226 key: train_mcc value: [0.24079318 0.28827833 0.29462783 0.33355056 0.27043219 0.26015295 0.38311227 0.27043219 0.30291963 0.29397824] mean value: 0.29382773671349693 key: test_fscore value: [0.33333333 0. 0. 0. 0.33333333 0. 0.28571429 0. 0. 0. ] mean value: 0.09523809523809525 key: train_fscore value: [0.25531915 0.26086957 0.29166667 0.35294118 0.28571429 0.25531915 0.36734694 0.28571429 0.32 0.26666667] mean value: 0.29415578830977346 key: test_precision value: [1. 0. 0. 0. 0.5 0. 0.33333333 0. 0. 0. ] mean value: 0.18333333333333332 key: train_precision value: [0.6 0.75 0.7 0.69230769 0.63636364 0.66666667 0.81818182 0.63636364 0.66666667 0.75 ] mean value: 0.6916550116550118 key: test_recall value: [0.2 0. 0. 0. 0.25 0. 0.25 0. 0. 0. ] mean value: 0.06999999999999999 key: train_recall value: [0.16216216 0.15789474 0.18421053 0.23684211 0.18421053 0.15789474 0.23684211 0.18421053 0.21052632 0.16216216] mean value: 0.18769559032716926 key: test_accuracy value: [0.81818182 0.80952381 0.80952381 0.80952381 0.80952381 0.80952381 0.76190476 0.66666667 0.76190476 0.76190476] mean value: 0.7818181818181819 key: train_accuracy value: [0.81481481 0.82105263 0.82105263 0.82631579 0.81578947 0.81578947 0.83684211 0.81578947 0.82105263 0.82631579] mean value: 0.8214814814814815 key: test_roc_auc value: [0.6 0.5 0.5 0.5 0.59558824 0.5 0.56617647 0.41176471 0.47058824 0.5 ] mean value: 0.5144117647058823 key: train_roc_auc value: [0.56792319 0.57236842 0.58223684 0.60526316 0.57894737 0.56907895 0.61184211 0.57894737 0.59210526 0.57454513] mean value: 0.5833257793397113 key: test_jcc value: [0.2 0. 0. 0. 0.2 0. 0.16666667 0. 0. 0. ] mean value: 0.056666666666666664 key: train_jcc value: [0.14634146 0.15 0.17073171 0.21428571 0.16666667 0.14634146 0.225 0.16666667 0.19047619 0.15384615] mean value: 0.17303560260877332 MCC on Blind test: 0.06 MCC on Training: 0.05 Running classifier: 15 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.00900578 0.00862479 0.00873613 0.00871778 0.00879097 0.00887942 0.00883794 0.00893497 0.00906873 0.00880027] mean value: 0.008839678764343262 key: score_time value: [0.0084765 0.00842643 0.00840664 0.00872684 0.00849938 0.00848293 0.00862479 0.00878143 0.00851369 0.00854707] mean value: 0.008548569679260255 key: test_mcc value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") [-0.17149859 -0.1573779 -0.1573779 0. 0.46097722 -0.10846523 0.66885605 0.25573908 -0.1573779 -0.125 ] mean value: 0.050847485714747176 key: train_mcc value: [0.26778157 0.28143328 0.13627059 0.27481054 0.26189246 0.33355056 0.29277002 0.21409215 0.27043219 0.28787945] mean value: 0.26209128262456155 key: test_fscore value: [0. 0. 0. 0. 0.4 0. 0.66666667 0.33333333 0. 0. ] mean value: 0.13999999999999999 key: train_fscore value: [0.31372549 0.31372549 0.17021277 0.33333333 0.30769231 0.35294118 0.33962264 0.24489796 0.28571429 0.32 ] mean value: 0.2981865450253226 key: test_precision value: [0. 0. 0. 0. 1. 0. 1. 0.5 0. 0. ] mean value: 0.25 key: train_precision value: [0.57142857 0.61538462 0.44444444 0.5625 0.57142857 0.69230769 0.6 0.54545455 0.63636364 0.61538462] mean value: 0.5854696692196691 key: test_recall value: [0. 0. 0. 0. 0.25 0. 0.5 0.25 0. 0. ] mean value: 0.1 key: train_recall value: [0.21621622 0.21052632 0.10526316 0.23684211 0.21052632 0.23684211 0.23684211 0.15789474 0.18421053 0.21621622] mean value: 0.20113798008534847 key: test_accuracy value: [0.68181818 0.71428571 0.71428571 0.80952381 0.85714286 0.76190476 0.9047619 0.80952381 0.71428571 0.71428571] mean value: 0.7681818181818182 key: train_accuracy value: [0.81481481 0.81578947 0.79473684 0.81052632 0.81052632 0.82631579 0.81578947 0.80526316 0.81578947 0.82105263] mean value: 0.8130604288499026 key: test_roc_auc value: [0.44117647 0.44117647 0.44117647 0.5 0.625 0.47058824 0.75 0.59558824 0.44117647 0.46875 ] mean value: 0.5174632352941176 key: train_roc_auc value: [0.58837127 0.58881579 0.53618421 0.59539474 0.58552632 0.60526316 0.59868421 0.5625 0.57894737 0.59176824] mean value: 0.5831455294303592 key: test_jcc value: [0. 0. 0. 0. 0.25 0. 0.5 0.2 0. 0. ] mean value: 0.095 key: train_jcc value: [0.18604651 0.18604651 0.09302326 0.2 0.18181818 0.21428571 0.20454545 0.13953488 0.16666667 0.19047619] mean value: 0.17624433705829054 MCC on Blind test: -0.05 MCC on Training: 0.05 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.01150393 0.0134809 0.01434517 0.01368022 0.01442909 0.01479602 0.01607203 0.01404238 0.01704597 0.01438546] mean value: 0.014378118515014648 key: score_time value: [0.00852847 0.01136994 0.01133728 0.01142764 0.01138663 0.01136398 0.01145935 0.01130009 0.01183748 0.0114255 ] mean value: 0.011143636703491212 key: test_mcc value: [ 0.0255655 0.49507377 0.01355815 0.46097722 -0.11891287 0.46097722 0.46097722 0.46097722 0.306786 -0.125 ] mean value: 0.24409794381891511 key: train_mcc value: [0.76475818 0.39115913 0.77459667 0.4633482 0.24174689 0.62279916 0.66666667 0.32209413 0.47937249 0.52798973] mean value: 0.5254531239013949 key: test_fscore value: [0.22222222 0.57142857 0.22222222 0.4 0.23529412 0.4 0.4 0.4 0.42105263 0. ] mean value: 0.3272219765099022 key: train_fscore value: [0.81081081 0.31111111 0.82051282 0.46153846 0.39583333 0.66666667 0.66666667 0.29787234 0.55474453 0.48979592] mean value: 0.5475552654980194 key: test_precision value: [0.25 0.66666667 0.2 1. 0.15384615 1. 1. 1. 0.26666667 0. ] mean value: 0.5537179487179487 key: train_precision value: [0.81081081 1. 0.8 0.85714286 0.24675325 0.84 1. 0.77777778 0.38383838 1. ] mean value: 0.7716323076323077 key: test_recall value: [0.2 0.5 0.25 0.25 0.5 0.25 0.25 0.25 1. 0. ] mean value: 0.34500000000000003 key: train_recall value: [0.81081081 0.18421053 0.84210526 0.31578947 1. 0.55263158 0.5 0.18421053 1. 0.32432432] mean value: 0.5714082503556188 key: test_accuracy value: [0.68181818 0.85714286 0.66666667 0.85714286 0.38095238 0.85714286 0.85714286 0.85714286 0.47619048 0.71428571] mean value: 0.7205627705627706 key: train_accuracy value: [0.92592593 0.83684211 0.92631579 0.85263158 0.38947368 0.88947368 0.9 0.82631579 0.67894737 0.86842105] mean value: 0.8094346978557505 key: test_roc_auc value: [0.51176471 0.72058824 0.50735294 0.625 0.42647059 0.625 0.625 0.625 0.67647059 0.46875 ] mean value: 0.581139705882353 key: train_roc_auc value: [0.88237909 0.59210526 0.89473684 0.65131579 0.61842105 0.76315789 0.75 0.58552632 0.79934211 0.66216216] mean value: 0.7199146514935989 key: test_jcc value: [0.125 0.4 0.125 0.25 0.13333333 0.25 0.25 0.25 0.26666667 0. ] mean value: 0.205 key: train_jcc value: [0.68181818 0.18421053 0.69565217 0.3 0.24675325 0.5 0.5 0.175 0.38383838 0.32432432] mean value: 0.39915968369629695 MCC on Blind test: -0.03 MCC on Training: 0.24 Running classifier: 17 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.01983714 0.01883459 0.02019405 0.01946974 0.01958871 0.01947951 0.02004051 0.01925254 0.01944566 0.01993132] mean value: 0.01960737705230713 key: score_time value: [0.01214647 0.01204658 0.01208353 0.01221204 0.01232696 0.01225758 0.01211238 0.01215839 0.01218128 0.0120573 ] mean value: 0.01215825080871582 key: test_mcc value: [ 0.41663055 0.17149859 -0.10846523 -0.1573779 -0.10846523 -0.10846523 -0.03834825 0.14852213 -0.1573779 -0.22821773] mean value: -0.017006619663108714 key: train_mcc value: [0.39749737 0.39115913 0.32879797 0.36115756 0.36115756 0.41931393 0.32879797 0.29329423 0.54201966 0.39771166] mean value: 0.38209070537826817 key: test_fscore value: [0.5 0.36363636 0. 0. 0. 0. 0.2 0.28571429 0. 0. ] mean value: 0.13493506493506496 key: train_fscore value: [0.31818182 0.31111111 0.23255814 0.27272727 0.27272727 0.34782609 0.23255814 0.19047619 0.50980392 0.31818182] mean value: 0.30061517710004 key: test_precision value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( [0.66666667 0.28571429 0. 0. 0. 0. 0.16666667 0.33333333 0. 0. ] mean value: 0.14523809523809522 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.4 0.5 0. 0. 0. 0. 0.25 0.25 0. 0. ] mean value: 0.13999999999999999 key: train_recall value: [0.18918919 0.18421053 0.13157895 0.15789474 0.15789474 0.21052632 0.13157895 0.10526316 0.34210526 0.18918919] mean value: 0.17994310099573257 key: test_accuracy value: [0.81818182 0.66666667 0.76190476 0.71428571 0.76190476 0.76190476 0.61904762 0.76190476 0.71428571 0.61904762] mean value: 0.71991341991342 key: train_accuracy value: [0.84126984 0.83684211 0.82631579 0.83157895 0.83157895 0.84210526 0.82631579 0.82105263 0.86842105 0.84210526] mean value: 0.8367585630743525 key: test_roc_auc value: [0.67058824 0.60294118 0.47058824 0.44117647 0.47058824 0.47058824 0.47794118 0.56617647 0.44117647 0.40625 ] mean value: 0.5018014705882352 key: train_roc_auc value: [0.59459459 0.59210526 0.56578947 0.57894737 0.57894737 0.60526316 0.56578947 0.55263158 0.67105263 0.59459459] mean value: 0.5899715504978662 key: test_jcc value: [0.33333333 0.22222222 0. 0. 0. 0. 0.11111111 0.16666667 0. 0. ] mean value: 0.08333333333333334 key: train_jcc value: [0.18918919 0.18421053 0.13157895 0.15789474 0.15789474 0.21052632 0.13157895 0.10526316 0.34210526 0.18918919] mean value: 0.17994310099573257 MCC on Blind test: 0.05 MCC on Training: -0.02 Running classifier: 18 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42))]) key: fit_time value: [0.61825752 0.66492939 0.60457897 0.62881064 0.5892036 0.6324985 0.60808969 0.68310857 0.60181642 0.5978756 ] mean value: 0.6229168891906738 key: score_time value: [0.15701485 0.16321039 0.14067888 0.12102771 0.18189287 0.1501143 0.17991328 0.12279081 0.13424611 0.13090491] mean value: 0.14817941188812256 key: test_mcc value: [-0.11834527 -0.10846523 0.25573908 0. -0.1573779 0. -0.10846523 0.25573908 -0.1573779 -0.18136906] mean value: -0.03199224185877884 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0. 0. 0.33333333 0. 0. 0. 0. 0.33333333 0. 0. ] mean value: 0.06666666666666667 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0. 0. 0.5 0. 0. 0. 0. 0.5 0. 0. ] mean value: 0.1 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0. 0. 0.25 0. 0. 0. 0. 0.25 0. 0. ] mean value: 0.05 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.72727273 0.76190476 0.80952381 0.80952381 0.71428571 0.80952381 0.76190476 0.80952381 0.71428571 0.66666667] mean value: 0.7584415584415585 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.47058824 0.47058824 0.59558824 0.5 0.44117647 0.5 0.47058824 0.59558824 0.44117647 0.4375 ] mean value: 0.49227941176470597 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0. 0. 0.2 0. 0. 0. 0. 0.2 0. 0. ] mean value: 0.04 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.05 MCC on Training: -0.03 Running classifier: 19 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea...age_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42))]) key: fit_time value: [0.94035554 0.92414212 0.93270612 0.89620304 0.90860963 0.94762135 0.93048286 0.92878175 0.93434811 0.92760563] mean value: 0.9270856142044067 key: score_time value: [0.25698113 0.25217128 0.19494915 0.20197415 0.24947953 0.25700092 0.1876657 0.21828222 0.16838765 0.26320934] mean value: 0.22501010894775392 key: test_mcc value: [-0.11834527 0. -0.10846523 0. 0. 0. 0. -0.10846523 0. -0.18136906] mean value: -0.05166447874289468 key: train_mcc value: [0.47906497 0.51929079 0.56407607 0.51371214 0.58084873 0.49579235 0.51929079 0.53674504 0.48990238 0.55109935] mean value: 0.5249822608181158 key: test_fscore value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_fscore value: [0.42553191 0.48 0.53846154 0.5 0.58181818 0.44897959 0.48 0.52830189 0.47058824 0.52 ] mean value: 0.49736813490966425 key: test_precision value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_precision value: [1. 1. 1. 0.92857143 0.94117647 1. 1. 0.93333333 0.92307692 1. ] mean value: 0.9726158155569922 key: test_recall value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_recall value: [0.27027027 0.31578947 0.36842105 0.34210526 0.42105263 0.28947368 0.31578947 0.36842105 0.31578947 0.35135135] mean value: 0.33584637268847795 key: test_accuracy value: [0.72727273 0.80952381 0.76190476 0.80952381 0.80952381 0.80952381 0.80952381 0.76190476 0.80952381 0.66666667] mean value: 0.7774891774891775 key: train_accuracy value: [0.85714286 0.86315789 0.87368421 0.86315789 0.87894737 0.85789474 0.86315789 0.86842105 0.85789474 0.87368421] mean value: 0.8657142857142859 key: test_roc_auc value: [0.47058824 0.5 0.47058824 0.5 0.5 0.5 0.5 0.47058824 0.5 0.4375 ] mean value: 0.48492647058823535 key: train_roc_auc value: [0.63513514 0.65789474 0.68421053 0.66776316 0.70723684 0.64473684 0.65789474 0.68092105 0.65460526 0.67567568] mean value: 0.6666073968705548 key: test_jcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) 0.0 key: train_jcc value: [0.27027027 0.31578947 0.36842105 0.33333333 0.41025641 0.28947368 0.31578947 0.35897436 0.30769231 0.35135135] mean value: 0.33213517160885575 MCC on Blind test: -0.07 MCC on Training: -0.05 Running classifier: 20 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.01248121 0.01736951 0.01704216 0.01716089 0.01722908 0.01654553 0.016752 0.01673961 0.0162518 0.01643133] mean value: 0.01640031337738037 key: score_time value: [0.01637387 0.01461864 0.01503372 0.01473761 0.01467085 0.01392794 0.01427007 0.01417017 0.01390767 0.02278805] mean value: 0.015449857711791993 key: test_mcc value: [ 0.10056599 0.49507377 -0.10846523 0.25573908 0.25573908 0.46097722 0.01355815 0.25573908 -0.1573779 -0.18136906] mean value: 0.13901801936522182 key: train_mcc value: [0.71313707 0.6617241 0.84746747 0.739574 0.72012057 0.739574 0.66324959 0.72012057 0.68262488 0.77100074] mean value: 0.7258592997460885 key: test_fscore value: [0.25 0.57142857 0. 0.33333333 0.33333333 0.4 0.22222222 0.33333333 0. 0. ] mean value: 0.2443650793650794 key: train_fscore value: [0.75 0.6984127 0.87323944 0.76190476 0.75757576 0.76190476 0.70769231 0.75757576 0.7 0.79365079] mean value: 0.7561956275336558 key: test_precision value: [0.33333333 0.66666667 0. 0.5 0.5 1. 0.2 0.5 0. 0. ] mean value: 0.37 key: train_precision value: [0.88888889 0.88 0.93939394 0.96 0.89285714 0.96 0.85185185 0.89285714 0.95454545 0.96153846] mean value: 0.9181932881932882 key: test_recall value: [0.2 0.5 0. 0.25 0.25 0.25 0.25 0.25 0. 0. ] mean value: 0.195 key: train_recall value: [0.64864865 0.57894737 0.81578947 0.63157895 0.65789474 0.63157895 0.60526316 0.65789474 0.55263158 0.67567568] mean value: 0.6455903271692744 key: test_accuracy value: [0.72727273 0.85714286 0.76190476 0.80952381 0.80952381 0.85714286 0.66666667 0.80952381 0.71428571 0.66666667] mean value: 0.767965367965368 key: train_accuracy value: [0.91534392 0.9 0.95263158 0.92105263 0.91578947 0.92105263 0.9 0.91578947 0.90526316 0.93157895] mean value: 0.9178501810080757 key: test_roc_auc value: [0.54117647 0.72058824 0.47058824 0.59558824 0.59558824 0.625 0.50735294 0.59558824 0.44117647 0.4375 ] mean value: 0.553014705882353 key: train_roc_auc value: [0.8144559 0.77960526 0.90131579 0.8125 0.81907895 0.8125 0.78947368 0.81907895 0.77302632 0.83456986] mean value: 0.8155604714621741 key: test_jcc value: [0.14285714 0.4 0. 0.2 0.2 0.25 0.125 0.2 0. 0. ] mean value: 0.15178571428571427 key: train_jcc value: [0.6 0.53658537 0.775 0.61538462 0.6097561 0.61538462 0.54761905 0.6097561 0.53846154 0.65789474] mean value: 0.6105842114667532 MCC on Blind test: 0.05 MCC on Training: 0.14 Running classifier: 21 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=3))]) key: fit_time value: [0.05365157 0.05246139 0.04141808 0.08203721 0.10444331 0.08991146 0.0985496 0.08819389 0.08900738 0.0714252 ] mean value: 0.07710990905761719 key: score_time value: [0.01221037 0.01197171 0.01173782 0.01671815 0.01743317 0.04054046 0.01737666 0.01777768 0.01433539 0.01206732] mean value: 0.017216873168945313 key: test_mcc value: [ 0. 0. -0.10846523 0. 0.25573908 0. 0.01355815 0.46097722 -0.10846523 -0.125 ] mean value: 0.03883439981531602 key: train_mcc value: [0.3598848 0.22276053 0.84746747 0.35379613 0.41110521 0.41247896 0.66324959 0.29462783 0.38311227 0.42633811] mean value: 0.43748208957003676 key: test_fscore value: [0. 0. 0. 0. 0.33333333 0. 0.22222222 0.4 0. 0. ] mean value: 0.09555555555555555 key: train_fscore value: [0.31111111 0.2173913 0.87323944 0.30434783 0.4 0.375 0.70769231 0.29166667 0.36734694 0.35555556] mean value: 0.42033511468556517 key: test_precision value: [0. 0. 0. 0. 0.5 0. 0.2 1. 0. 0. ] mean value: 0.16999999999999998 key: train_precision value: [0.875 0.625 0.93939394 0.875 0.83333333 0.9 0.85185185 0.7 0.81818182 1. ] mean value: 0.8417760942760942 key: test_recall value: [0. 0. 0. 0. 0.25 0. 0.25 0.25 0. 0. ] mean value: 0.075 key: train_recall value: [0.18918919 0.13157895 0.81578947 0.18421053 0.26315789 0.23684211 0.60526316 0.18421053 0.23684211 0.21621622] mean value: 0.30633001422475103 key: test_accuracy value: [0.77272727 0.80952381 0.76190476 0.80952381 0.80952381 0.80952381 0.66666667 0.85714286 0.76190476 0.71428571] mean value: 0.7772727272727273 key: train_accuracy value: [0.83597884 0.81052632 0.95263158 0.83157895 0.84210526 0.84210526 0.9 0.82105263 0.83684211 0.84736842] mean value: 0.8520189362294627 key: test_roc_auc value: [0.5 0.5 0.47058824 0.5 0.59558824 0.5 0.50735294 0.625 0.47058824 0.46875 ] mean value: 0.5137867647058825 key: train_roc_auc value: [0.59130512 0.55592105 0.90131579 0.58881579 0.625 0.61513158 0.78947368 0.58223684 0.61184211 0.60810811] mean value: 0.6469150071123754 key: test_jcc value: [0. 0. 0. 0. 0.2 0. 0.125 0.25 0. 0. ] mean value: 0.057499999999999996 key: train_jcc value: [0.18421053 0.12195122 0.775 0.17948718 0.25 0.23076923 0.54761905 0.17073171 0.225 0.21621622] mean value: 0.2900985127236732 MCC on Blind test: -0.07 MCC on Training: 0.04 Running classifier: 22 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.02696657 0.01112151 0.01021385 0.0103066 0.01092529 0.01082158 0.01085091 0.01079774 0.01115561 0.01127028] mean value: 0.012442994117736816 key: score_time value: [0.01490521 0.00970864 0.00892067 0.00887322 0.00884843 0.00958967 0.00971532 0.00880647 0.0090847 0.00949073] mean value: 0.009794306755065919 key: test_mcc value: [ 0. 0. 0. 0. 0. 0. 0. 0. -0.10846523 0. ] mean value: -0.010846522890932807 key: train_mcc value: [0. 0. 0. 0. 0.20628425 0. 0.14547859 0. 0.29329423 0.14791559] mean value: 0.0792972663170393 key: test_fscore value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_fscore value: [0. 0. 0. 0. 0.1 0. 0.05128205 0. 0.19047619 0.05263158] mean value: 0.03943898207056102 key: test_precision value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_precision value: [0. 0. 0. 0. 1. 0. 1. 0. 1. 1.] mean value: 0.4 key: test_recall value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_recall value: [0. 0. 0. 0. 0.05263158 0. 0.02631579 0. 0.10526316 0.02702703] mean value: 0.02112375533428165 key: test_accuracy value: [0.77272727 0.80952381 0.80952381 0.80952381 0.80952381 0.80952381 0.80952381 0.80952381 0.76190476 0.76190476] mean value: 0.7963203463203463 key: train_accuracy value: [0.8042328 0.8 0.8 0.8 0.81052632 0.8 0.80526316 0.8 0.82105263 0.81052632] mean value: 0.8051601225285436 key: test_roc_auc value: [0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.47058824 0.5 ] mean value: 0.4970588235294118 key: train_roc_auc value: [0.5 0.5 0.5 0.5 0.52631579 0.5 0.51315789 0.5 0.55263158 0.51351351] mean value: 0.5105618776671407 key: test_jcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_jcc value: [0. 0. 0. 0. 0.05263158 0. 0.02631579 0. 0.10526316 0.02702703] mean value: 0.02112375533428165 MCC on Blind test: 0.0 MCC on Training: -0.01 Running classifier: 23 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.01331115 0.01445389 0.01450014 0.01494884 0.01385236 0.01522875 0.01523209 0.01462626 0.01599312 0.01431918] mean value: 0.014646577835083007 key: score_time value: [0.00990176 0.01158762 0.01181602 0.01165915 0.01195002 0.01181817 0.01178837 0.01182437 0.01178813 0.01165962] mean value: 0.011579322814941406 key: test_mcc value: [ 0.3281388 -0.10846523 -0.34299717 0.25573908 -0.2300895 0.2300895 0.31506302 0.34299717 0.29827938 -0.125 ] mean value: 0.09637550458866095 key: train_mcc value: [0.63316166 0.36115756 0.64891109 0.38403531 0.2458811 0.57381904 0.63828474 0.50627391 0.74327065 0.42633811] mean value: 0.5161133179253208 key: test_fscore value: [0.5 0. 0. 0.33333333 0.21052632 0.4 0.46153846 0.44444444 0.44444444 0. ] mean value: 0.2794286999550158 key: train_fscore value: [0.69306931 0.27272727 0.71578947 0.34042553 0.39790576 0.63333333 0.70967742 0.58267717 0.78873239 0.35555556] mean value: 0.548989321238363 key: test_precision value: [0.42857143 0. 0. 0.5 0.13333333 0.33333333 0.33333333 0.28571429 0.4 0. ] mean value: 0.2414285714285714 key: train_precision value: [0.546875 1. 0.59649123 0.88888889 0.24836601 0.46341463 0.6 0.41573034 0.84848485 1. ] mean value: 0.6608250949740802 key: test_recall value: [0.6 0. 0. 0.25 0.5 0.5 0.75 1. 0.5 0. ] mean value: 0.41 key: train_recall value: [0.94594595 0.15789474 0.89473684 0.21052632 1. 1. 0.86842105 0.97368421 0.73684211 0.21621622] mean value: 0.7004267425320057 key: test_accuracy value: [0.72727273 0.76190476 0.47619048 0.80952381 0.28571429 0.71428571 0.66666667 0.52380952 0.76190476 0.71428571] mean value: 0.6441558441558441 key: train_accuracy value: [0.83597884 0.83157895 0.85789474 0.83684211 0.39473684 0.76842105 0.85789474 0.72105263 0.92105263 0.84736842] mean value: 0.7872820941241992 key: test_roc_auc value: [0.68235294 0.47058824 0.29411765 0.59558824 0.36764706 0.63235294 0.69852941 0.70588235 0.66176471 0.46875 ] mean value: 0.5577573529411766 key: train_roc_auc value: [0.87757824 0.57894737 0.87171053 0.60197368 0.62171053 0.85526316 0.86184211 0.81578947 0.85197368 0.60810811] mean value: 0.7544896870554766 key: test_jcc value: [0.33333333 0. 0. 0.2 0.11764706 0.25 0.3 0.28571429 0.28571429 0. ] mean value: 0.1772408963585434 key: train_jcc value: [0.53030303 0.15789474 0.55737705 0.20512821 0.24836601 0.46341463 0.55 0.41111111 0.65116279 0.21621622] mean value: 0.39909737866969075 MCC on Blind test: -0.02 MCC on Training: 0.1 Running classifier: 24 Model_name: XGBoost Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:463: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_CV['source_data'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:490: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_BT['source_data'] = 'BT' Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.09654236 0.0594511 0.07400751 0.06000233 0.05866742 0.05896354 0.06466699 0.06600881 0.07044291 0.08599877] mean value: 0.06947517395019531 key: score_time value: [0.01102138 0.01060843 0.0116992 0.01064706 0.01057315 0.01050735 0.01073241 0.01055527 0.01150346 0.0110755 ] mean value: 0.010892319679260253 key: test_mcc value: [ 0. -0.10846523 -0.19802951 -0.1573779 0.14852213 0. 0.49507377 0.29827938 0. 0.2125 ] mean value: 0.0690502649857901 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0. 0. 0. 0. 0.28571429 0. 0.57142857 0.44444444 0. 0.4 ] mean value: 0.1701587301587302 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0. 0. 0. 0. 0.33333333 0. 0.66666667 0.4 0. 0.4 ] mean value: 0.18 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0. 0. 0. 0. 0.25 0. 0.5 0.5 0. 0.4 ] mean value: 0.16499999999999998 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.77272727 0.76190476 0.66666667 0.71428571 0.76190476 0.80952381 0.85714286 0.76190476 0.80952381 0.71428571] mean value: 0.762987012987013 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.5 0.47058824 0.41176471 0.44117647 0.56617647 0.5 0.72058824 0.66176471 0.5 0.60625 ] mean value: 0.5378308823529412 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0. 0. 0. 0. 0.16666667 0. 0.4 0.28571429 0. 0.25 ] mean value: 0.11023809523809525 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.17 MCC on Training: 0.07 Extracting tts_split_name: 70_30 Total cols in each df: CV df: 8 metaDF: 17 Adding column: Model_name Total cols in bts df: BT_df: 8 First proceeding to rowbind CV and BT dfs: Final output should have: 25 columns Combinig 2 using pd.concat by row ~ rowbind Checking Dims of df to combine: Dim of CV: (24, 8) Dim of BT: (24, 8) 8 Number of Common columns: 8 These are: ['source_data', 'Precision', 'JCC', 'MCC', 'Recall', 'Accuracy', 'F1', 'ROC_AUC'] Concatenating dfs with different resampling methods [WF]: Split type: 70_30 No. of dfs combining: 2 PASS: 2 dfs successfully combined nrows in combined_df_wf: 48 ncols in combined_df_wf: 8 PASS: proceeding to merge metadata with CV and BT dfs Adding column: Model_name ========================================================= SUCCESS: Ran multiple classifiers ======================================================= ============================================================== Running several classification models (n): 24 List of models: ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('Gaussian NB', GaussianNB()) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('K-Nearest Neighbors', KNeighborsClassifier()) ('LDA', LinearDiscriminantAnalysis()) ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(cv=3, random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Multinomial', MultinomialNB()) ('Naive Bayes', BernoulliNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=12, random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Random Forest', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42)) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=3)) ('SVC', SVC(random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=12, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ================================================================ Running classifier: 1 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.18822837 0.18288732 0.18005323 0.17543817 0.18362045 0.18411136 0.1815269 0.17467809 0.17780495 0.18031549] mean value: 0.18086643218994142 key: score_time value: [0.01580119 0.01642346 0.01609898 0.01596403 0.01661992 0.01598263 0.01583433 0.01469803 0.01650262 0.01483226] mean value: 0.015875744819641113 key: test_mcc value: [0.73854895 0.88852332 0.88852332 0.71713717 0.88235294 0.76470588 0.58925565 0.61545745 0.53397044 0.69852941] mean value: 0.7317004529300096 key: train_mcc value: [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. 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Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... ÿÿÿÿÀ8€V@ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþÿÿÿÿÿÿÿÀð?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþÿÿÿÿÿÿÿÀ@ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþÿÿÿÿÿÿÿÀ@+,¡å\Ü?Îç+7®Ø?7@ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþÿÿÿÿÿÿÿÀ,@-.Zpffæ?ÇqÇqÜ?"@ÿÿÿÿÿÿBuilding estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.9s remaining: 1.8s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.9s remaining: 0.3s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.0s remaining: 1.9s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.0s remaining: 2.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.0s remaining: 2.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.0s remaining: 0.3s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.0s remaining: 2.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.1s remaining: 2.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.1s remaining: 2.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.1s remaining: 0.4s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.1s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.1s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.1s remaining: 0.4s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.1s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.1s remaining: 2.2s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.1s remaining: 2.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.1s remaining: 0.4s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.1s remaining: 0.4s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.1s remaining: 2.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.1s remaining: 0.4s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. 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[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [1. 0.99344255 0.99344255 0.99344255 0.99344255 0.99344255 1. 1. 1. 0.99346377] mean value: 0.9960676533285182 key: test_fscore value: [0.82758621 0.9375 0.94444444 0.86486486 0.94117647 0.88235294 0.8 0.82051282 0.77777778 0.84848485] mean value: 0.8644700374746014 key: train_fscore value: [1. 0.99672131 0.99669967 0.99672131 0.99672131 0.99672131 1. 1. 1. 0.99669967] mean value: 0.9980284585835634 key: test_precision value: [1. 1. 0.89473684 0.8 0.94117647 0.88235294 0.77777778 0.72727273 0.7 0.875 ] mean value: 0.8598316758920476 key: train_precision value: [1. 0.99346405 1. 0.99346405 0.99346405 0.99346405 1. 1. 1. 1. ] mean value: 0.9973856209150327 key: test_recall value: [0.70588235 0.88235294 1. 0.94117647 0.94117647 0.88235294 0.82352941 0.94117647 0.875 0.82352941] mean value: 0.8816176470588235 key: train_recall value: [1. 1. 0.99342105 1. 1. 1. 1. 1. 1. 0.99342105] mean value: 0.9986842105263157 key: test_accuracy value: [0.85294118 0.94117647 0.94117647 0.85294118 0.94117647 0.88235294 0.79411765 0.79411765 0.75757576 0.84848485] mean value: 0.8606060606060606 key: train_accuracy value: [1. 0.99671053 0.99671053 0.99671053 0.99671053 0.99671053 1. 1. 1. 0.99672131] mean value: 0.9980273943054356 key: test_roc_auc value: [0.85294118 0.94117647 0.94117647 0.85294118 0.94117647 0.88235294 0.79411765 0.79411765 0.76102941 0.84926471] mean value: 0.8610294117647058 key: train_roc_auc value: [1. 0.99671053 0.99671053 0.99671053 0.99671053 0.99671053 1. 1. 1. 0.99671053] mean value: 0.9980263157894737 key: test_jcc value: [0.70588235 0.88235294 0.89473684 0.76190476 0.88888889 0.78947368 0.66666667 0.69565217 0.63636364 0.73684211] mean value: 0.7658764053433591 key: train_jcc value: [1. 0.99346405 0.99342105 0.99346405 0.99346405 0.99346405 1. 1. 1. 0.99342105] mean value: 0.9960698314413484 MCC on Blind test: 0.18 MCC on Training: 0.73 Running classifier: 2 Model_name: Bagging Classifier Model func: BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3))]) key: fit_time value: [0.19779992 0.21679258 0.23105645 0.20797825 0.2313683 0.21837664 0.23345017 0.23422527 0.20387506 0.21242023] mean value: 0.2187342882156372 key: score_time value: [0.04196405 0.04773092 0.05813527 0.05095744 0.0701623 0.0354321 0.05258799 0.05620718 0.04242539 0.04731798] mean value: 0.05029206275939942 key: test_mcc value: [0.83666003 0.94280904 0.94280904 0.94280904 0.76470588 0.94280904 0.64705882 0.82495791 0.52029875 0.71008133] mean value: 0.8074998890154866 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.90322581 0.97142857 0.97142857 0.96969697 0.88235294 0.96969697 0.82352941 0.91428571 0.76470588 0.83870968] mean value: 0.9009060515701881 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.94444444 0.94444444 1. 0.88235294 1. 0.82352941 0.88888889 0.72222222 0.92857143] mean value: 0.9134453781512605 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.82352941 1. 1. 0.94117647 0.88235294 0.94117647 0.82352941 0.94117647 0.8125 0.76470588] mean value: 0.8930147058823529 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.91176471 0.97058824 0.97058824 0.97058824 0.88235294 0.97058824 0.82352941 0.91176471 0.75757576 0.84848485] mean value: 0.9017825311942959 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.91176471 0.97058824 0.97058824 0.97058824 0.88235294 0.97058824 0.82352941 0.91176471 0.75919118 0.85110294] mean value: 0.9022058823529413 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.82352941 0.94444444 0.94444444 0.94117647 0.78947368 0.94117647 0.7 0.84210526 0.61904762 0.72222222] mean value: 0.8267620030468326 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.16 MCC on Training: 0.81 Running classifier: 3 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.025949 0.026824 0.02773905 0.02550936 0.02707338 0.02650547 0.02218866 0.02432466 0.02482772 0.02546024] mean value: 0.025640153884887697 key: score_time value: [0.01011229 0.00972462 0.00963759 0.00957036 0.00903797 0.00903463 0.00937819 0.00913262 0.01006866 0.00929809] mean value: 0.009499502182006837 key: test_mcc value: [0.5976143 0.82495791 0.47140452 0.66575029 0.47140452 0.5976143 0.52941176 0.61545745 0.51470588 0.48126671] mean value: 0.5769587661131743 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.77419355 0.91428571 0.74285714 0.84210526 0.72727273 0.81081081 0.76470588 0.82051282 0.75 0.68965517] mean value: 0.7836399082050942 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.85714286 0.88888889 0.72222222 0.76190476 0.75 0.75 0.76470588 0.72727273 0.75 0.83333333] mean value: 0.7805470673117731 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.70588235 0.94117647 0.76470588 0.94117647 0.70588235 0.88235294 0.76470588 0.94117647 0.75 0.58823529] mean value: 0.7985294117647058 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.79411765 0.91176471 0.73529412 0.82352941 0.73529412 0.79411765 0.76470588 0.79411765 0.75757576 0.72727273] mean value: 0.7837789661319073 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.79411765 0.91176471 0.73529412 0.82352941 0.73529412 0.79411765 0.76470588 0.79411765 0.75735294 0.73161765] mean value: 0.7841911764705881 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.63157895 0.84210526 0.59090909 0.72727273 0.57142857 0.68181818 0.61904762 0.69565217 0.6 0.52631579] mean value: 0.6486128364389233 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.24 MCC on Training: 0.58 Running classifier: 4 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.00940442 0.00942492 0.009094 0.0102694 0.00971437 0.01001835 0.00900078 0.00947642 0.01007628 0.00913858] mean value: 0.009561753273010254 key: score_time value: [0.00878406 0.00856829 0.00862479 0.00904036 0.00845718 0.00850701 0.00916505 0.00931621 0.0084331 0.00872397] mean value: 0.008762001991271973 key: test_mcc value: [0.47140452 0.71713717 0.65158377 0.5976143 0.54470478 0.77005354 0.29617444 0.5976143 0.60731275 0.40105559] mean value: 0.5654655167360383 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.72727273 0.86486486 0.83333333 0.77419355 0.78947368 0.88888889 0.66666667 0.81081081 0.81081081 0.73684211] mean value: 0.7903157440508883 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.75 0.8 0.78947368 0.85714286 0.71428571 0.84210526 0.63157895 0.75 0.71428571 0.66666667] mean value: 0.7515538847117795 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.70588235 0.94117647 0.88235294 0.70588235 0.88235294 0.94117647 0.70588235 0.88235294 0.9375 0.82352941] mean value: 0.8408088235294118 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.73529412 0.85294118 0.82352941 0.79411765 0.76470588 0.88235294 0.64705882 0.79411765 0.78787879 0.6969697 ] mean value: 0.7778966131907309 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.73529412 0.85294118 0.82352941 0.79411765 0.76470588 0.88235294 0.64705882 0.79411765 0.79227941 0.69301471] mean value: 0.7779411764705882 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.57142857 0.76190476 0.71428571 0.63157895 0.65217391 0.8 0.5 0.68181818 0.68181818 0.58333333] mean value: 0.6578341605000644 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.02 MCC on Training: 0.57 Running classifier: 5 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.10678911 0.10795546 0.11049891 0.11176515 0.10877347 0.11149764 0.10955477 0.1061008 0.10942483 0.11329293] mean value: 0.10956530570983887 key: score_time value: [0.01849604 0.01887226 0.017838 0.01908422 0.0191958 0.01855206 0.01792502 0.01797676 0.01777434 0.01904726] mean value: 0.018476176261901855 key: test_mcc value: [0.70710678 0.88852332 0.88235294 0.82495791 0.70710678 0.76470588 0.5976143 0.70710678 0.63602941 0.69852941] mean value: 0.7414033523308606 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.84848485 0.9375 0.94117647 0.90909091 0.85714286 0.88235294 0.81081081 0.85714286 0.8125 0.84848485] mean value: 0.8704686542921836 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.875 1. 0.94117647 0.9375 0.83333333 0.88235294 0.75 0.83333333 0.8125 0.875 ] mean value: 0.8740196078431373 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.82352941 0.88235294 0.94117647 0.88235294 0.88235294 0.88235294 0.88235294 0.88235294 0.8125 0.82352941] mean value: 0.8694852941176471 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.85294118 0.94117647 0.94117647 0.91176471 0.85294118 0.88235294 0.79411765 0.85294118 0.81818182 0.84848485] mean value: 0.8696078431372548 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.85294118 0.94117647 0.94117647 0.91176471 0.85294118 0.88235294 0.79411765 0.85294118 0.81801471 0.84926471] mean value: 0.8696691176470589 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.73684211 0.88235294 0.88888889 0.83333333 0.75 0.78947368 0.68181818 0.75 0.68421053 0.73684211] mean value: 0.7733761766269506 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.01 MCC on Training: 0.74 Running classifier: 6 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.6635778 0.6487565 0.65102291 0.65286469 0.64994979 0.65324736 0.65156364 0.68253493 0.70508194 0.69074655] mean value: 0.6649346113204956 key: score_time value: [0.00916338 0.00911999 0.00934029 0.00920296 0.00921607 0.00925517 0.009202 0.01011968 0.00999784 0.01015615] mean value: 0.009477353096008301 key: test_mcc value: [0.88235294 0.82495791 0.88852332 0.83666003 0.70710678 0.88852332 0.76470588 0.73854895 0.65806217 0.69852941] mean value: 0.7887970699673149 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.94117647 0.90909091 0.94444444 0.91891892 0.85714286 0.9375 0.88235294 0.87179487 0.83333333 0.84848485] mean value: 0.8944239594974889 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.94117647 0.9375 0.89473684 0.85 0.83333333 1. 0.88235294 0.77272727 0.75 0.875 ] mean value: 0.8736826859930575 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.94117647 0.88235294 1. 1. 0.88235294 0.88235294 0.88235294 1. 0.9375 0.82352941] mean value: 0.9231617647058824 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.94117647 0.91176471 0.94117647 0.91176471 0.85294118 0.94117647 0.88235294 0.85294118 0.81818182 0.84848485] mean value: 0.8901960784313724 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.94117647 0.91176471 0.94117647 0.91176471 0.85294118 0.94117647 0.88235294 0.85294118 0.82169118 0.84926471] mean value: 0.890625 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.88888889 0.83333333 0.89473684 0.85 0.75 0.88235294 0.78947368 0.77272727 0.71428571 0.73684211] mean value: 0.8112640781990628 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.24 MCC on Training: 0.79 Running classifier: 7 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.00887346 0.00893879 0.00913382 0.00880003 0.00898862 0.00949478 0.00893641 0.00991249 0.00994992 0.01039147] mean value: 0.009341979026794433 key: score_time value: [0.00857115 0.00850964 0.00856471 0.00897002 0.00877261 0.00845814 0.00863671 0.00930572 0.00924802 0.00922275] mean value: 0.008825945854187011 key: test_mcc value: [0.65158377 0.54470478 0.58925565 0.41464421 0.66575029 0.53311399 0.49236596 0.58925565 0.10312858 0.45588235] mean value: 0.5039685235038915 key: train_mcc value: [0.57598214 0.68190989 0.62187595 0.64559158 0.62729848 0.58141882 0.62566382 0.61484328 0.58782584 0.63557581] mean value: 0.619798559455291 key: test_fscore value: [0.83333333 0.78947368 0.8 0.72222222 0.8 0.77777778 0.76923077 0.8 0.59459459 0.72727273] mean value: 0.7613905108641952 key: train_fscore value: [0.79750779 0.84735202 0.81875 0.83180428 0.82018927 0.79874214 0.81672026 0.81504702 0.8 0.82389937] mean value: 0.817001215748989 key: test_precision value: [0.78947368 0.71428571 0.77777778 0.68421053 0.92307692 0.73684211 0.68181818 0.77777778 0.52380952 0.75 ] mean value: 0.7359072214335372 key: train_precision value: [0.75739645 0.80473373 0.7797619 0.77714286 0.78787879 0.76506024 0.79874214 0.77844311 0.77777778 0.78915663] mean value: 0.7816093624683235 key: test_recall value: [0.88235294 0.88235294 0.82352941 0.76470588 0.70588235 0.82352941 0.88235294 0.82352941 0.6875 0.70588235] mean value: 0.7981617647058823 key: train_recall value: [0.84210526 0.89473684 0.86184211 0.89473684 0.85526316 0.83552632 0.83552632 0.85526316 0.82352941 0.86184211] mean value: 0.8560371517027864 key: test_accuracy value: [0.82352941 0.76470588 0.79411765 0.70588235 0.82352941 0.76470588 0.73529412 0.79411765 0.54545455 0.72727273] mean value: 0.7478609625668449 key: train_accuracy value: [0.78618421 0.83881579 0.80921053 0.81907895 0.8125 0.78947368 0.8125 0.80592105 0.79344262 0.81639344] mean value: 0.8083520276100087 key: test_roc_auc value: [0.82352941 0.76470588 0.79411765 0.70588235 0.82352941 0.76470588 0.73529412 0.79411765 0.54963235 0.72794118] mean value: 0.7483455882352941 key: train_roc_auc value: [0.78618421 0.83881579 0.80921053 0.81907895 0.8125 0.78947368 0.8125 0.80592105 0.79334365 0.81654197] mean value: 0.8083569831441348 key: test_jcc value: [0.71428571 0.65217391 0.66666667 0.56521739 0.66666667 0.63636364 0.625 0.66666667 0.42307692 0.57142857] mean value: 0.6187546149502671 key: train_jcc value: [0.66321244 0.73513514 0.69312169 0.71204188 0.69518717 0.66492147 0.69021739 0.68783069 0.66666667 0.70053476] mean value: 0.6908869285210721 MCC on Blind test: -0.11 MCC on Training: 0.5 Running classifier: 8 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.08410525 0.08661008 0.08264542 0.09079885 0.0881474 0.09704137 0.08914781 0.06429482 0.04946041 0.09797835] mean value: 0.08302297592163085 key: score_time value: [0.02103639 0.02065039 0.02077913 0.02067327 0.0206387 0.02061486 0.02068424 0.01307321 0.01307964 0.02083349] mean value: 0.01920633316040039 key: test_mcc value: [0.82495791 0.88852332 0.83666003 0.76470588 0.70710678 0.76470588 0.41464421 0.71713717 0.64207079 0.63944497] mean value: 0.7199956944124326 key: train_mcc value: [0.96762892 0.96085908 0.97376851 0.96085908 0.96085908 0.96762892 0.96729368 0.96762892 0.96098262 0.96740206] mean value: 0.9654910889834738 key: test_fscore value: [0.91428571 0.94444444 0.91891892 0.88235294 0.85714286 0.88235294 0.72222222 0.86486486 0.82352941 0.83333333] mean value: 0.8643447649330002 key: train_fscore value: [0.98381877 0.98051948 0.9869281 0.98051948 0.98051948 0.98381877 0.98371336 0.98381877 0.98064516 0.98371336] mean value: 0.9828014728201259 key: test_precision value: [0.88888889 0.89473684 0.85 0.88235294 0.83333333 0.88235294 0.68421053 0.8 0.77777778 0.78947368] mean value: 0.8283126934984522 key: train_precision value: [0.96815287 0.96794872 0.98051948 0.96794872 0.96794872 0.96815287 0.97419355 0.96815287 0.96815287 0.97419355] mean value: 0.9705364196107981 key: test_recall value: [0.94117647 1. 1. 0.88235294 0.88235294 0.88235294 0.76470588 0.94117647 0.875 0.88235294] mean value: 0.9051470588235293 key: train_recall value: [1. 0.99342105 0.99342105 0.99342105 0.99342105 1. 0.99342105 1. 0.99346405 0.99342105] mean value: 0.9953990368077056 key: test_accuracy value: [0.91176471 0.94117647 0.91176471 0.88235294 0.85294118 0.88235294 0.70588235 0.85294118 0.81818182 0.81818182] mean value: 0.8577540106951872 key: train_accuracy value: [0.98355263 0.98026316 0.98684211 0.98026316 0.98026316 0.98355263 0.98355263 0.98355263 0.98032787 0.98360656] mean value: 0.9825776531492666 key: test_roc_auc value: [0.91176471 0.94117647 0.91176471 0.88235294 0.85294118 0.88235294 0.70588235 0.85294118 0.81985294 0.81617647] mean value: 0.8577205882352941 key: train_roc_auc value: [0.98355263 0.98026316 0.98684211 0.98026316 0.98026316 0.98355263 0.98355263 0.98355263 0.98028466 0.98363863] mean value: 0.9825765393876849 key: test_jcc value: [0.84210526 0.89473684 0.85 0.78947368 0.75 0.78947368 0.56521739 0.76190476 0.7 0.71428571] mean value: 0.7657197341179035 key: train_jcc value: [0.96815287 0.96178344 0.97419355 0.96178344 0.96178344 0.96815287 0.96794872 0.96815287 0.96202532 0.96794872] mean value: 0.966192521793768 MCC on Blind test: -0.08 MCC on Training: 0.72 Running classifier: 9 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.02148986 0.01034451 0.01017189 0.00995779 0.00999761 0.00918889 0.00904036 0.01011753 0.00896454 0.00986648] mean value: 0.010913944244384766 key: score_time value: [0.02659607 0.01357913 0.01568985 0.01680136 0.01730371 0.01317072 0.01198936 0.01464057 0.0121932 0.0123868 ] mean value: 0.015435075759887696 key: test_mcc value: [0.82495791 0.66575029 0.73854895 0.58925565 0.66575029 0.76470588 0.47809144 0.53311399 0.71008133 0.58285506] mean value: 0.6553110792382718 key: train_mcc value: [0.75061933 0.74206366 0.73294875 0.74620251 0.7613557 0.74786421 0.75061933 0.77459667 0.75275229 0.73582588] mean value: 0.7494848321620198 key: test_fscore value: [0.91428571 0.84210526 0.87179487 0.8 0.84210526 0.88235294 0.75675676 0.77777778 0.85714286 0.81081081] mean value: 0.8355132256061047 key: train_fscore value: [0.88 0.8757764 0.87066246 0.87804878 0.8847352 0.87878788 0.88 0.89096573 0.88145897 0.87227414] mean value: 0.8792709561806005 key: test_precision value: [0.88888889 0.76190476 0.77272727 0.77777778 0.76190476 0.88235294 0.7 0.73684211 0.78947368 0.75 ] mean value: 0.7821872193853617 key: train_precision value: [0.8265896 0.82941176 0.83636364 0.81818182 0.84023669 0.81460674 0.8265896 0.84615385 0.82386364 0.82840237] mean value: 0.8290399687347737 key: test_recall value: [0.94117647 0.94117647 1. 0.82352941 0.94117647 0.88235294 0.82352941 0.82352941 0.9375 0.88235294] mean value: 0.8996323529411765 key: train_recall value: [0.94078947 0.92763158 0.90789474 0.94736842 0.93421053 0.95394737 0.94078947 0.94078947 0.94771242 0.92105263] mean value: 0.936218610251118 key: test_accuracy value: [0.91176471 0.82352941 0.85294118 0.79411765 0.82352941 0.88235294 0.73529412 0.76470588 0.84848485 0.78787879] mean value: 0.8224598930481284 key: train_accuracy value: [0.87171053 0.86842105 0.86513158 0.86842105 0.87828947 0.86842105 0.87171053 0.88486842 0.87213115 0.86557377] mean value: 0.8714678602243314 key: test_roc_auc value: [0.91176471 0.82352941 0.85294118 0.79411765 0.82352941 0.88235294 0.73529412 0.76470588 0.85110294 0.78492647] mean value: 0.8224264705882354 key: train_roc_auc value: [0.87171053 0.86842105 0.86513158 0.86842105 0.87828947 0.86842105 0.87171053 0.88486842 0.87188252 0.86575507] mean value: 0.8714611283109734 key: test_jcc value: [0.84210526 0.72727273 0.77272727 0.66666667 0.72727273 0.78947368 0.60869565 0.63636364 0.75 0.68181818] mean value: 0.7202395811663547 key: train_jcc value: [0.78571429 0.77900552 0.77094972 0.7826087 0.79329609 0.78378378 0.78571429 0.80337079 0.78804348 0.77348066] mean value: 0.7845967313543423 MCC on Blind test: -0.08 MCC on Training: 0.66 Running classifier: 10 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.02695036 0.03021073 0.03916097 0.0586834 0.07203436 0.07941294 0.07341385 0.0336051 0.03437304 0.03264284] mean value: 0.048048758506774904 key: score_time value: [0.01196742 0.01202822 0.0120914 0.02328444 0.01938677 0.02360749 0.01200271 0.01206732 0.01189566 0.01201439] mean value: 0.01503458023071289 key: test_mcc value: [0.54470478 0.70710678 0.82495791 0.78679579 0.71713717 0.82495791 0.77005354 0.64549722 0.83276554 0.77919372] mean value: 0.7433170373799426 key: train_mcc value: [0.96729368 0.94745044 0.96729368 0.9544639 0.96060947 0.94129888 0.94080983 0.96762892 0.95460628 0.96073963] mean value: 0.9562194735426173 key: test_fscore value: [0.78947368 0.85714286 0.91428571 0.89473684 0.86486486 0.90909091 0.88888889 0.82926829 0.91428571 0.89473684] mean value: 0.8756774609662928 key: train_fscore value: [0.98371336 0.97385621 0.98371336 0.97734628 0.98039216 0.97087379 0.9704918 0.98381877 0.97749196 0.98039216] mean value: 0.9782089832618472 key: test_precision value: [0.71428571 0.83333333 0.88888889 0.80952381 0.8 0.9375 0.84210526 0.70833333 0.84210526 0.80952381] mean value: 0.818559941520468 key: train_precision value: [0.97419355 0.96753247 0.97419355 0.96178344 0.97402597 0.95541401 0.96732026 0.96815287 0.96202532 0.97402597] mean value: 0.9678667408723551 key: test_recall value: [0.88235294 0.88235294 0.94117647 1. 0.94117647 0.88235294 0.94117647 1. 1. 1. ] mean value: 0.9470588235294117 key: train_recall value: [0.99342105 0.98026316 0.99342105 0.99342105 0.98684211 0.98684211 0.97368421 1. 0.99346405 0.98684211] mean value: 0.9888200894392846 key: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( test_accuracy value: [0.76470588 0.85294118 0.91176471 0.88235294 0.85294118 0.91176471 0.88235294 0.79411765 0.90909091 0.87878788] mean value: 0.8640819964349375 key: train_accuracy value: [0.98355263 0.97368421 0.98355263 0.97697368 0.98026316 0.97039474 0.97039474 0.98355263 0.97704918 0.98032787] mean value: 0.977974547023296 key: test_roc_auc value: [0.76470588 0.85294118 0.91176471 0.88235294 0.85294118 0.91176471 0.88235294 0.79411765 0.91176471 0.875 ] mean value: 0.8639705882352942 key: train_roc_auc value: [0.98355263 0.97368421 0.98355263 0.97697368 0.98026316 0.97039474 0.97039474 0.98355263 0.97699518 0.98034916] mean value: 0.9779712762297901 key: test_jcc value: [0.65217391 0.75 0.84210526 0.80952381 0.76190476 0.83333333 0.8 0.70833333 0.84210526 0.80952381] mean value: 0.7809003486978315 key: train_jcc value: [0.96794872 0.94904459 0.96794872 0.9556962 0.96153846 0.94339623 0.94267516 0.96815287 0.95597484 0.96153846] mean value: 0.9573914242153363 MCC on Blind test: 0.04 MCC on Training: 0.74 Running classifier: 11 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.04327154 0.03628445 0.03783536 0.0356741 0.03559613 0.03694773 0.0371964 0.03560758 0.03690529 0.0471046 ] mean value: 0.03824231624603271 key: score_time value: [0.01201558 0.01198411 0.01170588 0.01191926 0.01188588 0.01192355 0.01180291 0.01192498 0.01193786 0.01207042] mean value: 0.011917042732238769 key: test_mcc value: [0.76470588 0.70710678 0.61545745 0.65158377 0.53311399 0.82495791 0.53311399 0.70710678 0.71008133 0.71008133] mean value: 0.675730922605052 key: train_mcc value: [0.8158601 0.78954203 0.78291168 0.80263158 0.82923455 0.80270107 0.80922804 0.80325763 0.75081699 0.80327657] mean value: 0.7989460248027849 key: test_fscore value: [0.88235294 0.84848485 0.82051282 0.83333333 0.77777778 0.90909091 0.77777778 0.85714286 0.85714286 0.83870968] mean value: 0.8402325799859007 key: train_fscore value: [0.90849673 0.89542484 0.89108911 0.90131579 0.91333333 0.90196078 0.90491803 0.90322581 0.87581699 0.90131579] mean value: 0.8996897206835321 key: test_precision value: [0.88235294 0.875 0.72727273 0.78947368 0.73684211 0.9375 0.73684211 0.83333333 0.78947368 0.92857143] mean value: 0.8236662009301329 key: train_precision value: [0.9025974 0.88961039 0.89403974 0.90131579 0.92567568 0.8961039 0.90196078 0.88607595 0.87581699 0.90131579] mean value: 0.8974512405178938 key: test_recall value: [0.88235294 0.82352941 0.94117647 0.88235294 0.82352941 0.88235294 0.82352941 0.88235294 0.9375 0.76470588] mean value: 0.8643382352941178 key: train_recall value: [0.91447368 0.90131579 0.88815789 0.90131579 0.90131579 0.90789474 0.90789474 0.92105263 0.87581699 0.90131579] mean value: 0.9020553835569316 key: test_accuracy value: [0.88235294 0.85294118 0.79411765 0.82352941 0.76470588 0.91176471 0.76470588 0.85294118 0.84848485 0.84848485] mean value: 0.8344028520499108 key: train_accuracy value: [0.90789474 0.89473684 0.89144737 0.90131579 0.91447368 0.90131579 0.90460526 0.90131579 0.87540984 0.90163934] mean value: 0.8994154443485763 key: test_roc_auc value: [0.88235294 0.85294118 0.79411765 0.82352941 0.76470588 0.91176471 0.76470588 0.85294118 0.85110294 0.85110294] mean value: 0.8349264705882353 key: train_roc_auc value: [0.90789474 0.89473684 0.89144737 0.90131579 0.91447368 0.90131579 0.90460526 0.90131579 0.8754085 0.90163829] mean value: 0.8994152046783626 key: test_jcc value: [0.78947368 0.73684211 0.69565217 0.71428571 0.63636364 0.83333333 0.63636364 0.75 0.75 0.72222222] mean value: 0.726453650595527 key: train_jcc value: [0.83233533 0.81065089 0.80357143 0.82035928 0.8404908 0.82142857 0.82634731 0.82352941 0.77906977 0.82035928] mean value: 0.8178142061931334 MCC on Blind test: 0.0 MCC on Training: 0.68 Running classifier: 12 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(cv=3, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(cv=3, random_state=42))]) key: fit_time value: [0.47367048 0.46276689 0.46306896 0.46132588 0.63622403 0.45943475 0.47818732 0.47470331 0.59811711 0.5055213 ] mean value: 0.5013020038604736 key: score_time value: [0.01207304 0.01188254 0.01196194 0.01194119 0.01199126 0.01192546 0.01259947 0.0119164 0.01190662 0.01195955] mean value: 0.012015748023986816 key: test_mcc value: [0.65158377 0.77005354 0.56582515 0.71713717 0.65158377 0.82495791 0.5976143 0.73854895 0.58739713 0.63602941] mean value: 0.6740731088663987 key: train_mcc value: [0.95396801 0.96060947 1. 0.84884956 1. 0.95396801 1. 1. 1. 0.96073626] mean value: 0.9678131317483947 key: test_fscore value: [0.83333333 0.875 0.8 0.86486486 0.83333333 0.90909091 0.81081081 0.87179487 0.8 0.82352941] mean value: 0.8421757534992829 key: train_fscore value: [0.97704918 0.98013245 1. 0.92358804 1. 0.97689769 1. 1. 1. 0.98013245] mean value: 0.9837799810626209 key: test_precision value: [0.78947368 0.93333333 0.69565217 0.8 0.78947368 0.9375 0.75 0.77272727 0.73684211 0.82352941] mean value: 0.8028531665422566 key: train_precision value: [0.97385621 0.98666667 1. 0.93288591 1. 0.98013245 1. 1. 1. 0.98666667] mean value: 0.9860207898855053 key: test_recall value: [0.88235294 0.82352941 0.94117647 0.94117647 0.88235294 0.88235294 0.88235294 1. 0.875 0.82352941] mean value: 0.8933823529411764 key: train_recall value: [0.98026316 0.97368421 1. 0.91447368 1. 0.97368421 1. 1. 1. 0.97368421] mean value: 0.9815789473684209 key: test_accuracy value: [0.82352941 0.88235294 0.76470588 0.85294118 0.82352941 0.91176471 0.79411765 0.85294118 0.78787879 0.81818182] mean value: 0.8311942959001781 key: train_accuracy value: [0.97697368 0.98026316 1. 0.92434211 1. 0.97697368 1. 1. 1. 0.98032787] mean value: 0.9838880500431408 key: test_roc_auc value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( [0.82352941 0.88235294 0.76470588 0.85294118 0.82352941 0.91176471 0.79411765 0.85294118 0.79044118 0.81801471] mean value: 0.8314338235294116 key: train_roc_auc value: [0.97697368 0.98026316 1. 0.92434211 1. 0.97697368 1. 1. 1. 0.98030616] mean value: 0.9838858789129687 key: test_jcc value: [0.71428571 0.77777778 0.66666667 0.76190476 0.71428571 0.83333333 0.68181818 0.77272727 0.66666667 0.7 ] mean value: 0.728946608946609 key: train_jcc value: [0.95512821 0.96103896 1. 0.85802469 1. 0.95483871 1. 1. 1. 0.96103896] mean value: 0.969006952824157 MCC on Blind test: 0.09 MCC on Training: 0.67 Running classifier: 13 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [1.49510574 1.40479398 1.28295755 1.38218474 1.33832788 1.41806841 1.42826128 1.34546113 1.40499282 1.29089403] mean value: 1.3791047573089599 key: score_time value: [0.01255417 0.01225924 0.01208949 0.01220822 0.01634574 0.01211858 0.01215816 0.01224113 0.01308918 0.02210259] mean value: 0.013716650009155274 key: test_mcc value: [0.77005354 0.82495791 0.71713717 0.83666003 0.58925565 0.82495791 0.44008623 0.83666003 0.64207079 0.69852941] mean value: 0.7180368666717634 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.88888889 0.90909091 0.86486486 0.91891892 0.8 0.90909091 0.75 0.91891892 0.82352941 0.84848485] mean value: 0.8631787670022965 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.84210526 0.9375 0.8 0.85 0.77777778 0.9375 0.65217391 0.85 0.77777778 0.875 ] mean value: 0.8299834731756928 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.94117647 0.88235294 0.94117647 1. 0.82352941 0.88235294 0.88235294 1. 0.875 0.82352941] mean value: 0.9051470588235293 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.88235294 0.91176471 0.85294118 0.91176471 0.79411765 0.91176471 0.70588235 0.91176471 0.81818182 0.84848485] mean value: 0.8549019607843137 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.88235294 0.91176471 0.85294118 0.91176471 0.79411765 0.91176471 0.70588235 0.91176471 0.81985294 0.84926471] mean value: 0.8551470588235294 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.8 0.83333333 0.76190476 0.85 0.66666667 0.83333333 0.6 0.85 0.7 0.73684211] mean value: 0.7632080200501253 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.04 MCC on Training: 0.72 Running classifier: 14 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.01285386 0.01270294 0.00957227 0.01027441 0.00982308 0.00981045 0.00993347 0.01006532 0.00953531 0.00930285] mean value: 0.010387396812438965 key: score_time value: [0.01159525 0.01108479 0.00969076 0.00921464 0.00834513 0.00867987 0.00942636 0.00925827 0.00890803 0.00912023] mean value: 0.009532332420349121 key: test_mcc value: [0.58925565 0.44008623 0.49236596 0.66575029 0.31434731 0.53311399 0.23570226 0.66575029 0.46471292 0.40987872] mean value: 0.48109636093490005 key: train_mcc value: [0.47970161 0.49819867 0.50532094 0.52301826 0.53004547 0.51861886 0.51765858 0.53550866 0.56090541 0.51060384] mean value: 0.5179580296151152 key: test_fscore value: [0.78787879 0.75 0.76923077 0.84210526 0.7 0.77777778 0.60606061 0.84210526 0.74285714 0.66666667] mean value: 0.748468227678754 key: train_fscore value: [0.75609756 0.76307692 0.76687117 0.77258567 0.77639752 0.77300613 0.77160494 0.77742947 0.78456592 0.77108434] mean value: 0.7712719629080268 key: test_precision value: [0.8125 0.65217391 0.68181818 0.76190476 0.60869565 0.73684211 0.625 0.76190476 0.68421053 0.76923077] mean value: 0.7094280671654813 key: train_precision value: [0.70454545 0.71676301 0.7183908 0.73372781 0.73529412 0.72413793 0.72674419 0.74251497 0.7721519 0.71111111] mean value: 0.7285381290207612 key: test_recall value: [0.76470588 0.88235294 0.88235294 0.94117647 0.82352941 0.82352941 0.58823529 0.94117647 0.8125 0.58823529] mean value: 0.8047794117647058 key: train_recall value: [0.81578947 0.81578947 0.82236842 0.81578947 0.82236842 0.82894737 0.82236842 0.81578947 0.79738562 0.84210526] mean value: 0.8198701410388717 key: test_accuracy value: [0.79411765 0.70588235 0.73529412 0.82352941 0.64705882 0.76470588 0.61764706 0.82352941 0.72727273 0.6969697 ] mean value: 0.7336007130124778 key: train_accuracy value: [0.73684211 0.74671053 0.75 0.75986842 0.76315789 0.75657895 0.75657895 0.76644737 0.78032787 0.75081967] mean value: 0.7567331751509923 key: test_roc_auc value: [0.79411765 0.70588235 0.73529412 0.82352941 0.64705882 0.76470588 0.61764706 0.82352941 0.72977941 0.70036765] mean value: 0.7341911764705882 key: train_roc_auc value: [0.73684211 0.74671053 0.75 0.75986842 0.76315789 0.75657895 0.75657895 0.76644737 0.78027176 0.75111799] mean value: 0.7567573959408324 key: test_jcc value: [0.65 0.6 0.625 0.72727273 0.53846154 0.63636364 0.43478261 0.72727273 0.59090909 0.5 ] mean value: 0.6030062328975372 key: train_jcc value: [0.60784314 0.61691542 0.62189055 0.62944162 0.63451777 0.63 0.6281407 0.63589744 0.64550265 0.62745098] mean value: 0.6277600263576926 MCC on Blind test: -0.24 MCC on Training: 0.48 Running classifier: 15 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.01369929 0.01032305 0.01061416 0.01042008 0.01054597 0.01043034 0.01027846 0.01015377 0.01024938 0.01043224] mean value: 0.01071467399597168 key: score_time value: [0.00965881 0.00947165 0.00968003 0.00943351 0.00884509 0.00923038 0.00952172 0.0097537 0.00962567 0.00971937] mean value: 0.00949399471282959 key: test_mcc value: [0.35355339 0.77005354 0.65158377 0.78679579 0.41464421 0.47809144 0.54470478 0.58925565 0.46471292 0.3985267 ] mean value: 0.5451922192159511 key: train_mcc value: [0.64433695 0.58218174 0.57689314 0.56462516 0.58510845 0.59119576 0.60162084 0.58305002 0.63709783 0.62908067] mean value: 0.5995190559535889 key: test_fscore value: [0.66666667 0.88888889 0.83333333 0.89473684 0.72222222 0.75675676 0.78947368 0.8 0.74285714 0.6875 ] mean value: 0.7782435537040799 key: train_fscore value: [0.83076923 0.8 0.79876161 0.79384615 0.80368098 0.80615385 0.80877743 0.80124224 0.82716049 0.82424242] mean value: 0.8094634405832958 key: test_precision value: [0.6875 0.84210526 0.78947368 0.80952381 0.68421053 0.7 0.71428571 0.77777778 0.68421053 0.73333333] mean value: 0.7422420634920635 key: train_precision value: [0.78034682 0.76190476 0.75438596 0.74566474 0.75287356 0.75722543 0.77245509 0.75882353 0.78362573 0.76404494] mean value: 0.7631350578301588 key: test_recall value: [0.64705882 0.94117647 0.88235294 1. 0.76470588 0.82352941 0.88235294 0.82352941 0.8125 0.64705882] mean value: 0.8224264705882354 key: train_recall value: [0.88815789 0.84210526 0.84868421 0.84868421 0.86184211 0.86184211 0.84868421 0.84868421 0.87581699 0.89473684] mean value: 0.8619238046095632 key: test_accuracy value: [0.67647059 0.88235294 0.82352941 0.88235294 0.70588235 0.73529412 0.76470588 0.79411765 0.72727273 0.6969697 ] mean value: 0.7688948306595366 key: train_accuracy value: [0.81907895 0.78947368 0.78618421 0.77960526 0.78947368 0.79276316 0.79934211 0.78947368 0.81639344 0.80983607] mean value: 0.7971624245038826 key: test_roc_auc value: [0.67647059 0.88235294 0.82352941 0.88235294 0.70588235 0.73529412 0.76470588 0.79411765 0.72977941 0.69852941] mean value: 0.7693014705882352 key: train_roc_auc value: [0.81907895 0.78947368 0.78618421 0.77960526 0.78947368 0.79276316 0.79934211 0.78947368 0.81619797 0.81011352] mean value: 0.797170622635019 key: test_jcc value: [0.5 0.8 0.71428571 0.80952381 0.56521739 0.60869565 0.65217391 0.66666667 0.59090909 0.52380952] mean value: 0.6431281761716543 key: train_jcc value: [0.71052632 0.66666667 0.66494845 0.65816327 0.67179487 0.67525773 0.67894737 0.66839378 0.70526316 0.70103093] mean value: 0.6800992541658406 MCC on Blind test: -0.1 MCC on Training: 0.55 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.01286578 0.01886749 0.01687551 0.01755476 0.01783371 0.01709175 0.01938319 0.01746273 0.01927209 0.01942372] mean value: 0.017663073539733887 key: score_time value: [0.0088191 0.01214719 0.01197553 0.01212645 0.01211166 0.01197863 0.01209092 0.01211977 0.01208496 0.01216435] mean value: 0.011761856079101563 key: test_mcc value: [0.77005354 0.66575029 0.5547002 0.73854895 0.56582515 0.73854895 0.53311399 0.83666003 0.63944497 0.5573704 ] mean value: 0.6600016456972183 key: train_mcc value: [0.86501158 0.69565894 0.69871519 0.81692173 0.71059869 0.6186278 0.86230877 0.77631579 0.84417286 0.69032211] mean value: 0.7578653465710519 key: test_fscore value: [0.875 0.84210526 0.64 0.87179487 0.8 0.82758621 0.77777778 0.91891892 0.8 0.71428571] mean value: 0.8067468752831729 key: train_fscore value: [0.92783505 0.85386819 0.80155642 0.91025641 0.85875706 0.71966527 0.93203883 0.88815789 0.9122807 0.784 ] mean value: 0.8588415842434776 key: test_precision value: [0.93333333 0.76190476 1. 0.77272727 0.69565217 1. 0.73684211 0.85 0.85714286 0.90909091] mean value: 0.8516693413375334 key: train_precision value: [0.97122302 0.75634518 0.98095238 0.8875 0.75247525 0.98850575 0.91719745 0.88815789 0.98484848 1. ] mean value: 0.9127205406665905 key: test_recall value: [0.82352941 0.94117647 0.47058824 1. 0.94117647 0.70588235 0.82352941 1. 0.75 0.58823529] mean value: 0.8044117647058824 key: train_recall value: [0.88815789 0.98026316 0.67763158 0.93421053 1. 0.56578947 0.94736842 0.88815789 0.8496732 0.64473684] mean value: 0.8375988992088063 key: test_accuracy value: [0.88235294 0.82352941 0.73529412 0.85294118 0.76470588 0.85294118 0.76470588 0.91176471 0.81818182 0.75757576] mean value: 0.8163992869875223 key: train_accuracy value: [0.93092105 0.83223684 0.83223684 0.90789474 0.83552632 0.77960526 0.93092105 0.88815789 0.91803279 0.82295082] mean value: 0.8678483606557377 key: test_roc_auc value: [0.88235294 0.82352941 0.73529412 0.85294118 0.76470588 0.85294118 0.76470588 0.91176471 0.81617647 0.76286765] mean value: 0.8167279411764706 key: train_roc_auc value: [0.93092105 0.83223684 0.83223684 0.90789474 0.83552632 0.77960526 0.93092105 0.88815789 0.91825765 0.82236842] mean value: 0.86781260749914 key: test_jcc value: [0.77777778 0.72727273 0.47058824 0.77272727 0.66666667 0.70588235 0.63636364 0.85 0.66666667 0.55555556] mean value: 0.6829500891265596 key: train_jcc value: [0.86538462 0.745 0.66883117 0.83529412 0.75247525 0.5620915 0.87272727 0.79881657 0.83870968 0.64473684] mean value: 0.7584067012954797 MCC on Blind test: -0.01 MCC on Training: 0.66 Running classifier: 17 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.0246861 0.0252502 0.02583599 0.02510262 0.02626491 0.02556634 0.02557898 0.02559257 0.02582169 0.02497244] mean value: 0.025467181205749513 key: score_time value: [0.01239967 0.0124073 0.01252151 0.0125823 0.01258111 0.01239157 0.01259136 0.01250196 0.01256418 0.01238704] mean value: 0.012492799758911132 key: test_mcc value: [0.88235294 1. 1. 1. 1. 0.94280904 0.94280904 1. 0.94117647 1. ] mean value: 0.9709147494928831 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.94117647 1. 1. 1. 1. 0.96969697 0.97142857 1. 0.96969697 1. ] mean value: 0.9851998981410747 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.94117647 1. 1. 1. 1. 1. 0.94444444 1. 0.94117647 1. ] mean value: 0.9826797385620916 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.94117647 1. 1. 1. 1. 0.94117647 1. 1. 1. 1. ] mean value: 0.9882352941176471 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.94117647 1. 1. 1. 1. 0.97058824 0.97058824 1. 0.96969697 1. ] mean value: 0.9852049910873439 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.94117647 1. 1. 1. 1. 0.97058824 0.97058824 1. 0.97058824 1. ] mean value: 0.9852941176470589 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.88888889 1. 1. 1. 1. 0.94117647 0.94444444 1. 0.94117647 1. ] mean value: 0.9715686274509803 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.2 MCC on Training: 0.97 Running classifier: 18 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42))]) key: fit_time value: [0.71069884 0.63146448 0.63383675 0.6520896 0.64679027 0.62319708 0.656461 0.64355326 0.70877004 0.65512514] mean value: 0.6561986446380615 key: score_time value: [0.14625502 0.17390203 0.14388227 0.16593385 0.15495539 0.15484476 0.16942549 0.20279312 0.16661716 0.19194484] mean value: 0.16705539226531982 key: test_mcc value: [0.70710678 0.82495791 0.94280904 0.88852332 0.70710678 0.82495791 0.5976143 0.82495791 0.76384284 0.69852941] mean value: 0.7780406209956948 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.84848485 0.90909091 0.97142857 0.9375 0.85714286 0.90909091 0.81081081 0.91428571 0.88235294 0.84848485] mean value: 0.8888672409995939 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.875 0.9375 0.94444444 1. 0.83333333 0.9375 0.75 0.88888889 0.83333333 0.875 ] mean value: 0.8875 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.82352941 0.88235294 1. 0.88235294 0.88235294 0.88235294 0.88235294 0.94117647 0.9375 0.82352941] mean value: 0.89375 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.85294118 0.91176471 0.97058824 0.94117647 0.85294118 0.91176471 0.79411765 0.91176471 0.87878788 0.84848485] mean value: 0.8874331550802138 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.85294118 0.91176471 0.97058824 0.94117647 0.85294118 0.91176471 0.79411765 0.91176471 0.88051471 0.84926471] mean value: 0.8876838235294118 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.73684211 0.83333333 0.94444444 0.88235294 0.75 0.83333333 0.68181818 0.84210526 0.78947368 0.73684211] mean value: 0.8030545392000501 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.01 MCC on Training: 0.78 Running classifier: 19 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea...age_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42))]) key: fit_time value: [0.96037555 0.95205784 0.9621985 0.99058294 0.95973539 0.96723604 0.95773649 0.96089554 0.98774505 0.95610762] mean value: 0.965467095375061 key: score_time value: [0.186625 0.21947169 0.13536096 0.21788669 0.23079872 0.30656004 0.22883439 0.24442506 0.23113799 0.20597386] mean value: 0.22070744037628173 key: test_mcc value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( [0.70710678 0.77005354 0.83666003 0.88852332 0.88235294 0.82495791 0.58925565 0.77005354 0.64207079 0.69852941] mean value: 0.7609563913863742 key: train_mcc value: [0.94097277 0.93493921 0.96762892 0.94769663 0.95496057 0.94769663 0.9544639 0.94769663 0.947871 0.96139484] mean value: 0.9505321091477338 key: test_fscore value: [0.84848485 0.875 0.91891892 0.9375 0.94117647 0.90909091 0.8 0.88888889 0.82352941 0.84848485] mean value: 0.8791074296221353 key: train_fscore value: [0.97009967 0.96644295 0.98327759 0.97333333 0.97643098 0.97333333 0.97658863 0.97333333 0.97350993 0.97986577] mean value: 0.97462155235479 key: test_precision value: [0.875 0.93333333 0.85 1. 0.94117647 0.9375 0.77777778 0.84210526 0.77777778 0.875 ] mean value: 0.8809670622635017 key: train_precision value: [0.97986577 0.98630137 1. 0.98648649 1. 0.98648649 0.99319728 0.98648649 0.98657718 1. ] mean value: 0.9905401061254173 key: test_recall value: [0.82352941 0.82352941 1. 0.88235294 0.94117647 0.88235294 0.82352941 0.94117647 0.875 0.82352941] mean value: 0.8816176470588235 key: train_recall value: [0.96052632 0.94736842 0.96710526 0.96052632 0.95394737 0.96052632 0.96052632 0.96052632 0.96078431 0.96052632] mean value: 0.9592363261093911 key: test_accuracy value: [0.85294118 0.88235294 0.91176471 0.94117647 0.94117647 0.91176471 0.79411765 0.88235294 0.81818182 0.84848485] mean value: 0.8784313725490195 key: train_accuracy value: [0.97039474 0.96710526 0.98355263 0.97368421 0.97697368 0.97368421 0.97697368 0.97368421 0.97377049 0.98032787] mean value: 0.9750150992234685 key: test_roc_auc value: [0.85294118 0.88235294 0.91176471 0.94117647 0.94117647 0.91176471 0.79411765 0.88235294 0.81985294 0.84926471] mean value: 0.8786764705882352 key: train_roc_auc value: [0.97039474 0.96710526 0.98355263 0.97368421 0.97697368 0.97368421 0.97697368 0.97368421 0.97381321 0.98026316] mean value: 0.9750128998968007 key: test_jcc value: [0.73684211 0.77777778 0.85 0.88235294 0.88888889 0.83333333 0.66666667 0.8 0.7 0.73684211] mean value: 0.7872703818369453 key: train_jcc value: [0.94193548 0.93506494 0.96710526 0.94805195 0.95394737 0.94805195 0.95424837 0.94805195 0.9483871 0.96052632] mean value: 0.9505370673247434 MCC on Blind test: -0.01 MCC on Training: 0.76 Running classifier: 20 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.03547549 0.03636551 0.03870535 0.03570032 0.03607225 0.04733634 0.03700328 0.0372014 0.04593873 0.03794742] mean value: 0.03877460956573486 key: score_time value: [0.0205512 0.02319407 0.02376604 0.02286053 0.02253389 0.02344179 0.02060795 0.02026296 0.02117491 0.01641154] mean value: 0.021480488777160644 key: test_mcc value: [0.70710678 0.76470588 0.71713717 0.66575029 0.77005354 0.82495791 0.53311399 0.73854895 0.81985294 0.75735294] mean value: 0.7298580385759055 key: train_mcc value: [0.91465185 0.88833093 0.91449348 0.88833093 0.89481431 0.90820927 0.90820927 0.90149139 0.90827779 0.88901325] mean value: 0.9015822460394688 key: test_fscore value: [0.85714286 0.88235294 0.86486486 0.84210526 0.88888889 0.90909091 0.77777778 0.87179487 0.90909091 0.88235294] mean value: 0.8685462224161915 key: train_fscore value: [0.95765472 0.94352159 0.95709571 0.94352159 0.94771242 0.95454545 0.95454545 0.95114007 0.95394737 0.94498382] mean value: 0.9508668201796187 key: test_precision value: [0.83333333 0.88235294 0.8 0.76190476 0.84210526 0.9375 0.73684211 0.77272727 0.88235294 0.88235294] mean value: 0.8331471559915833 key: train_precision value: [0.9483871 0.95302013 0.9602649 0.95302013 0.94155844 0.94230769 0.94230769 0.94193548 0.9602649 0.92993631] mean value: 0.9473002782332351 key: test_recall value: [0.88235294 0.88235294 0.94117647 0.94117647 0.94117647 0.88235294 0.82352941 1. 0.9375 0.88235294] mean value: 0.9113970588235294 key: train_recall value: [0.96710526 0.93421053 0.95394737 0.93421053 0.95394737 0.96710526 0.96710526 0.96052632 0.94771242 0.96052632] mean value: 0.9546396628826971 key: test_accuracy value: [0.85294118 0.88235294 0.85294118 0.82352941 0.88235294 0.91176471 0.76470588 0.85294118 0.90909091 0.87878788] mean value: 0.8611408199643493 key: train_accuracy value: [0.95723684 0.94407895 0.95723684 0.94407895 0.94736842 0.95394737 0.95394737 0.95065789 0.95409836 0.9442623 ] mean value: 0.9506913287316653 key: test_roc_auc value: [0.85294118 0.88235294 0.85294118 0.82352941 0.88235294 0.91176471 0.76470588 0.85294118 0.90992647 0.87867647] mean value: 0.8612132352941175 key: train_roc_auc value: [0.95723684 0.94407895 0.95723684 0.94407895 0.94736842 0.95394737 0.95394737 0.95065789 0.95411937 0.94431545] mean value: 0.9506987444100445 key: test_jcc value: [0.75 0.78947368 0.76190476 0.72727273 0.8 0.83333333 0.63636364 0.77272727 0.83333333 0.78947368] mean value: 0.7693882433356117 key: train_jcc value: [0.91875 0.89308176 0.91772152 0.89308176 0.90062112 0.91304348 0.91304348 0.9068323 0.91194969 0.89570552] mean value: 0.9063830620677711 MCC on Blind test: -0.04 MCC on Training: 0.73 Running classifier: 21 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=3))]) key: fit_time value: [0.16295099 0.1130662 0.12066746 0.07172537 0.06090879 0.05261707 0.0930202 0.17499208 0.13269782 0.14283442] mean value: 0.11254804134368897 key: score_time value: [0.03237963 0.022295 0.01787186 0.0121758 0.0122087 0.012918 0.02081776 0.01962614 0.02360892 0.02161264] mean value: 0.0195514440536499 key: test_mcc value: [0.65158377 0.76470588 0.77005354 0.83666003 0.77005354 0.82495791 0.53311399 0.6 0.81985294 0.83103851] mean value: 0.7402020106069351 key: train_mcc value: [0.96712619 0.88833093 0.96060947 0.93453417 0.94080983 0.94080983 0.90820927 0.94097277 0.90827779 0.94116889] mean value: 0.933084915817253 key: test_fscore value: [0.83333333 0.88235294 0.88888889 0.91891892 0.88888889 0.90909091 0.77777778 0.80952381 0.90909091 0.91891892] mean value: 0.8736785295608825 key: train_fscore value: [0.98360656 0.94352159 0.98039216 0.96753247 0.97029703 0.9704918 0.95454545 0.97068404 0.95394737 0.97068404] mean value: 0.9665702510580709 key: test_precision value: [0.78947368 0.88235294 0.84210526 0.85 0.84210526 0.9375 0.73684211 0.68 0.88235294 0.85 ] mean value: 0.8292732198142415 key: train_precision value: [0.98039216 0.95302013 0.97402597 0.95512821 0.97350993 0.96732026 0.94230769 0.96129032 0.9602649 0.96129032] mean value: 0.9628549903589088 key: test_recall value: [0.88235294 0.88235294 0.94117647 1. 0.94117647 0.88235294 0.82352941 1. 0.9375 1. ] mean value: 0.9290441176470589 key: train_recall value: [0.98684211 0.93421053 0.98684211 0.98026316 0.96710526 0.97368421 0.96710526 0.98026316 0.94771242 0.98026316] mean value: 0.9704291365669073 key: test_accuracy value: [0.82352941 0.88235294 0.88235294 0.91176471 0.88235294 0.91176471 0.76470588 0.76470588 0.90909091 0.90909091] mean value: 0.8641711229946523 key: train_accuracy value: [0.98355263 0.94407895 0.98026316 0.96710526 0.97039474 0.97039474 0.95394737 0.97039474 0.95409836 0.9704918 ] mean value: 0.9664721742881796 key: test_roc_auc value: [0.82352941 0.88235294 0.88235294 0.91176471 0.88235294 0.91176471 0.76470588 0.76470588 0.90992647 0.90625 ] mean value: 0.8639705882352942 key: train_roc_auc value: [0.98355263 0.94407895 0.98026316 0.96710526 0.97039474 0.97039474 0.95394737 0.97039474 0.95411937 0.97052374] mean value: 0.9664774681802545 key: test_jcc value: [0.71428571 0.78947368 0.8 0.85 0.8 0.83333333 0.63636364 0.68 0.83333333 0.85 ] mean value: 0.7786789701526543 key: train_jcc value: [0.96774194 0.89308176 0.96153846 0.93710692 0.94230769 0.94267516 0.91304348 0.94303797 0.91194969 0.94303797] mean value: 0.9355521040973527 MCC on Blind test: -0.04 MCC on Training: 0.74 Running classifier: 22 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.01963997 0.01601744 0.0146482 0.01409435 0.01597905 0.01679945 0.01564074 0.01559854 0.0158155 0.0155077 ] mean value: 0.015974092483520507 key: score_time value: [0.01456118 0.01134515 0.01087499 0.01008415 0.01127076 0.0117712 0.01061821 0.01013541 0.01042914 0.01031256] mean value: 0.011140275001525878 key: test_mcc value: [0.82495791 0.82495791 0.70710678 0.71713717 0.65158377 0.82495791 0.35355339 0.65158377 0.45588235 0.58739713] mean value: 0.6599118080643137 key: train_mcc value: [0.79008947 0.78291168 0.78372626 0.80290964 0.8158601 0.79606986 0.83597873 0.80922804 0.81639147 0.8229628 ] mean value: 0.805612805709789 key: test_fscore value: [0.91428571 0.90909091 0.85714286 0.86486486 0.8125 0.90909091 0.66666667 0.83333333 0.72727273 0.77419355] mean value: 0.8268441530135078 key: train_fscore value: [0.89261745 0.89108911 0.88888889 0.9 0.90728477 0.89768977 0.91638796 0.90491803 0.90849673 0.91089109] mean value: 0.901826379844119 key: test_precision value: [0.88888889 0.9375 0.83333333 0.8 0.86666667 0.9375 0.6875 0.78947368 0.70588235 0.85714286] mean value: 0.8303887783183448 key: train_precision value: [0.9109589 0.89403974 0.91034483 0.91216216 0.91333333 0.90066225 0.93197279 0.90196078 0.90849673 0.91390728] mean value: 0.9097838804169986 key: test_recall value: [0.94117647 0.88235294 0.88235294 0.94117647 0.76470588 0.88235294 0.64705882 0.88235294 0.75 0.70588235] mean value: 0.8279411764705882 key: train_recall value: [0.875 0.88815789 0.86842105 0.88815789 0.90131579 0.89473684 0.90131579 0.90789474 0.90849673 0.90789474] mean value: 0.894139146886825 key: test_accuracy value: [0.91176471 0.91176471 0.85294118 0.85294118 0.82352941 0.91176471 0.67647059 0.82352941 0.72727273 0.78787879] mean value: 0.8279857397504455 key: train_accuracy value: [0.89473684 0.89144737 0.89144737 0.90131579 0.90789474 0.89802632 0.91776316 0.90460526 0.90819672 0.91147541] mean value: 0.9026908973252803 key: test_roc_auc value: [0.91176471 0.91176471 0.85294118 0.85294118 0.82352941 0.91176471 0.67647059 0.82352941 0.72794118 0.79044118] mean value: 0.8283088235294118 key: train_roc_auc value: [0.89473684 0.89144737 0.89144737 0.90131579 0.90789474 0.89802632 0.91776316 0.90460526 0.90819573 0.91146371] mean value: 0.9026896284829722 key: test_jcc value: [0.84210526 0.83333333 0.75 0.76190476 0.68421053 0.83333333 0.5 0.71428571 0.57142857 0.63157895] mean value: 0.712218045112782 key: train_jcc value: [0.80606061 0.80357143 0.8 0.81818182 0.83030303 0.81437126 0.84567901 0.82634731 0.83233533 0.83636364] mean value: 0.8213213424041766 MCC on Blind test: -0.09 MCC on Training: 0.66 Running classifier: 23 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.01117301 0.01477098 0.01694751 0.01515055 0.01625705 0.01602149 0.01608443 0.01581597 0.01860523 0.01762915] mean value: 0.015845537185668945 key: score_time value: [0.00829387 0.010782 0.01069307 0.01141262 0.01137614 0.01139164 0.01138663 0.01135373 0.01287413 0.01165771] mean value: 0.01112215518951416 key: test_mcc value: [0.71713717 0.6 0.51639778 0.58925565 0.36369648 0.70710678 0.54470478 0.69156407 0.54879547 0.69742172] mean value: 0.597607991002585 key: train_mcc value: [0.77644535 0.58747999 0.80891679 0.79388419 0.57735027 0.83641374 0.81317982 0.78983234 0.61346033 0.81350515] mean value: 0.7410467962503469 key: test_fscore value: [0.83870968 0.80952381 0.7804878 0.8 0.5 0.84848485 0.78947368 0.85 0.7804878 0.85714286] mean value: 0.7854310486537492 key: train_fscore value: [0.86956522 0.8042328 0.90634441 0.88888889 0.66666667 0.91961415 0.90909091 0.89759036 0.816 0.90853659] mean value: 0.858652999186831 key: test_precision value: [0.92857143 0.68 0.66666667 0.77777778 0.85714286 0.875 0.71428571 0.73913043 0.64 0.83333333] mean value: 0.7711908212560387 key: train_precision value: [0.96774194 0.67256637 0.83798883 0.94117647 1. 0.89937107 0.86826347 0.82777778 0.68918919 0.84659091] mean value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:463: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_CV['source_data'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:490: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_BT['source_data'] = 'BT' 0.8550666022863324 key: test_recall value: [0.76470588 1. 0.94117647 0.82352941 0.35294118 0.82352941 0.88235294 1. 1. 0.88235294] mean value: 0.8470588235294118 key: train_recall value: [0.78947368 1. 0.98684211 0.84210526 0.5 0.94078947 0.95394737 0.98026316 1. 0.98026316] mean value: 0.8973684210526315 key: test_accuracy value: [0.85294118 0.76470588 0.73529412 0.79411765 0.64705882 0.85294118 0.76470588 0.82352941 0.72727273 0.84848485] mean value: 0.7811051693404634 key: train_accuracy value: [0.88157895 0.75657895 0.89802632 0.89473684 0.75 0.91776316 0.90460526 0.88815789 0.77377049 0.90163934] mean value: 0.8566857204486625 key: test_roc_auc value: [0.85294118 0.76470588 0.73529412 0.79411765 0.64705882 0.85294118 0.76470588 0.82352941 0.73529412 0.84742647] mean value: 0.7818014705882352 key: train_roc_auc value: [0.88157895 0.75657895 0.89802632 0.89473684 0.75 0.91776316 0.90460526 0.88815789 0.77302632 0.90189628] mean value: 0.8566369969040247 key: test_jcc value: [0.72222222 0.68 0.64 0.66666667 0.33333333 0.73684211 0.65217391 0.73913043 0.64 0.75 ] mean value: 0.6560368675311467 key: train_jcc value: [0.76923077 0.67256637 0.82872928 0.8 0.5 0.85119048 0.83333333 0.81420765 0.68918919 0.83240223] mean value: 0.7590849306303236 MCC on Blind test: 0.17 MCC on Training: 0.6 Running classifier: 24 Model_name: XGBoost Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.12800169 0.08236909 0.08474946 0.0803082 0.08423734 0.08713794 0.07815909 0.09169006 0.20624161 0.07394981] mean value: 0.09968442916870117 key: score_time value: [0.01358104 0.01105666 0.01080275 0.0115695 0.01077962 0.01106477 0.01101708 0.01114297 0.01139951 0.01103735] mean value: 0.011345124244689942 key: test_mcc value: [0.82495791 0.71713717 0.94280904 0.82495791 0.77005354 0.88852332 0.70710678 0.88852332 0.69852941 0.64207079] mean value: 0.790466918928323 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.90909091 0.83870968 0.97142857 0.91428571 0.88888889 0.9375 0.84848485 0.94444444 0.84848485 0.8125 ] mean value: 0.891381790252758 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.9375 0.92857143 0.94444444 0.88888889 0.84210526 1. 0.875 0.89473684 0.82352941 0.86666667] mean value: 0.9001442945599294 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.88235294 0.76470588 1. 0.94117647 0.94117647 0.88235294 0.82352941 1. 0.875 0.76470588] mean value: 0.8875 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.91176471 0.85294118 0.97058824 0.91176471 0.88235294 0.94117647 0.85294118 0.94117647 0.84848485 0.81818182] mean value: 0.8931372549019606 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.91176471 0.85294118 0.97058824 0.91176471 0.88235294 0.94117647 0.85294118 0.94117647 0.84926471 0.81985294] mean value: 0.8933823529411764 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.83333333 0.72222222 0.94444444 0.84210526 0.8 0.88235294 0.73684211 0.89473684 0.73684211 0.68421053] mean value: 0.8077089783281733 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.41 MCC on Training: 0.79 Extracting tts_split_name: 70_30 Total cols in each df: CV df: 8 metaDF: 17 Adding column: Model_name Total cols in bts df: BT_df: 8 First proceeding to rowbind CV and BT dfs: Final output should have: 25 columns Combinig 2 using pd.concat by row ~ rowbind Checking Dims of df to combine: Dim of CV: (24, 8) Dim of BT: (24, 8) 8 Number of Common columns: 8 These are: ['source_data', 'Precision', 'JCC', 'MCC', 'Recall', 'Accuracy', 'F1', 'ROC_AUC'] Concatenating dfs with different resampling methods [WF]: Split type: 70_30 No. of dfs combining: 2 PASS: 2 dfs successfully combined nrows in combined_df_wf: 48 ncols in combined_df_wf: 8 PASS: proceeding to merge metadata with CV and BT dfs Adding column: Model_name ========================================================= SUCCESS: Ran multiple classifiers ======================================================= ============================================================== Running several classification models (n): 24 List of models: ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('Gaussian NB', GaussianNB()) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('K-Nearest Neighbors', KNeighborsClassifier()) ('LDA', LinearDiscriminantAnalysis()) ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(cv=3, random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Multinomial', MultinomialNB()) ('Naive Bayes', BernoulliNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=12, random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Random Forest', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42)) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=3)) ('SVC', SVC(random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=12, random_state=42)) [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. 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Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... p”ð4À¹ÐÀ¹”PÆÐß?BêF<óß?¶s@rðÀ×?H}mÔyñÝ?SÀBuilding estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.7s remaining: 1.3s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.7s remaining: 1.4s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.7s remaining: 1.4s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.7s remaining: 1.4s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.7s remaining: 0.2s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.7s remaining: 1.4s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.7s remaining: 1.4s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.7s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.7s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.7s remaining: 0.2s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.7s remaining: 1.5s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.8s remaining: 1.5s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.8s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.8s remaining: 0.3s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.8s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.8s remaining: 0.3s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.8s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.8s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.8s remaining: 0.3s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.8s remaining: 1.6s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.8s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.8s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.8s remaining: 1.6s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.8s remaining: 0.3s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.8s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.8s remaining: 0.3s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.8s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.8s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.8s remaining: 0.3s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.8s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. 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[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ================================================================ Running classifier: 1 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.13996625 0.12833381 0.12710905 0.12937403 0.1277771 0.12825084 0.12925315 0.12784481 0.134547 0.13232684] mean value: 0.13047828674316406 key: score_time value: [0.01610136 0.0163002 0.01511025 0.01504827 0.01538682 0.01530933 0.01583195 0.01510882 0.01626086 0.01540947] mean value: 0.0155867338180542 key: test_mcc value: [0.83666003 0.88852332 0.88852332 0.88852332 0.83666003 1. 0.73854895 0.73854895 0.88561489 0.69742172] mean value: 0.839902450263898 key: train_mcc value: [1. 1. 1. 1. 0.99344255 1. 1. 1. 0.99346377 1. ] mean value: 0.9986906324377449 key: test_fscore value: [0.91891892 0.94444444 0.94444444 0.94444444 0.91891892 1. 0.87179487 0.87179487 0.94117647 0.85714286] mean value: 0.9213080242492009 key: train_fscore value: [1. 1. 1. 1. 0.99672131 1. 1. 1. 0.99674267 1. ] mean value: 0.9993463982485181 key: test_precision value: [0.85 0.89473684 0.89473684 0.89473684 0.85 1. 0.77272727 0.77272727 0.88888889 0.83333333] mean value: 0.8651887293992558 key: train_precision value: [1. 1. 1. 1. 0.99346405 1. 1. 1. 0.99350649 1. ] mean value: 0.9986970545794076 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.88235294] mean value: 0.9882352941176471 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.91176471 0.94117647 0.94117647 0.94117647 0.91176471 1. 0.85294118 0.85294118 0.93939394 0.84848485] mean value: 0.9140819964349376 key: train_accuracy value: [1. 1. 1. 1. 0.99671053 1. 1. 1. 0.99672131 1. ] mean value: 0.9993431837791199 key: test_roc_auc value: [0.91176471 0.94117647 0.94117647 0.94117647 0.91176471 1. 0.85294118 0.85294118 0.94117647 0.84742647] mean value: 0.9141544117647058 key: train_roc_auc value: [1. 1. 1. 1. 0.99671053 1. 1. 1. 0.99671053 1. ] mean value: 0.9993421052631579 key: test_jcc value: [0.85 0.89473684 0.89473684 0.89473684 0.85 1. 0.77272727 0.77272727 0.88888889 0.75 ] mean value: 0.8568553960659224 key: train_jcc value: [1. 1. 1. 1. 0.99346405 1. 1. 1. 0.99350649 1. ] mean value: 0.9986970545794076 MCC on Blind test: 0.03 MCC on Training: 0.84 Running classifier: 2 Model_name: Bagging Classifier Model func: BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3))]) key: fit_time value: [0.18159461 0.19413567 0.17590737 0.19937396 0.19601846 0.17910862 0.20022917 0.19704986 0.21840429 0.17849135] mean value: 0.1920313358306885 key: score_time value: [0.06882596 0.04303384 0.07237959 0.05289125 0.05728126 0.0579071 0.06314445 0.06863976 0.04577756 0.03856468] mean value: 0.056844544410705564 key: test_mcc value: [0.88852332 0.88852332 0.78679579 0.88852332 0.94280904 1. 0.78679579 0.88852332 0.94117647 0.75735294] mean value: 0.8769023304840211 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.94444444 0.94444444 0.89473684 0.94444444 0.97142857 1. 0.89473684 0.94444444 0.96969697 0.88235294] mean value: 0.9390729944290317 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.89473684 0.89473684 0.80952381 0.89473684 0.94444444 1. 0.80952381 0.89473684 0.94117647 0.88235294] mean value: 0.8965968843677821 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.88235294] mean value: 0.9882352941176471 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.94117647 0.94117647 0.88235294 0.94117647 0.97058824 1. 0.88235294 0.94117647 0.96969697 0.87878788] mean value: 0.9348484848484848 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.94117647 0.94117647 0.88235294 0.94117647 0.97058824 1. 0.88235294 0.94117647 0.97058824 0.87867647] mean value: 0.9349264705882353 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.89473684 0.89473684 0.80952381 0.89473684 0.94444444 1. 0.80952381 0.89473684 0.94117647 0.78947368] mean value: 0.8873089586711875 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.06 MCC on Training: 0.88 Running classifier: 3 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.03762817 0.01606703 0.01528144 0.01577735 0.01554275 0.01514292 0.01682997 0.01663303 0.01709628 0.01644444] mean value: 0.018244338035583497 key: score_time value: [0.00975823 0.00881362 0.00883126 0.00887418 0.00885415 0.00908041 0.00949836 0.00933886 0.00918913 0.00973201] mean value: 0.009197020530700683 key: test_mcc value: [0.94280904 1. 0.73854895 0.78679579 0.78679579 0.94280904 0.64549722 0.69156407 0.88561489 0.69742172] mean value: 0.8117856521057958 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.97142857 1. 0.87179487 0.89473684 0.89473684 0.97142857 0.82926829 0.85 0.94117647 0.85714286] mean value: 0.9081713319276561 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.94444444 1. 0.77272727 0.80952381 0.80952381 0.94444444 0.70833333 0.73913043 0.88888889 0.83333333] mean value: 0.8450349771001946 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.88235294] mean value: 0.9882352941176471 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.97058824 1. 0.85294118 0.88235294 0.88235294 0.97058824 0.79411765 0.82352941 0.93939394 0.84848485] mean value: 0.8964349376114082 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.97058824 1. 0.85294118 0.88235294 0.88235294 0.97058824 0.79411765 0.82352941 0.94117647 0.84742647] mean value: 0.8965073529411764 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.94444444 1. 0.77272727 0.80952381 0.80952381 0.94444444 0.70833333 0.73913043 0.88888889 0.75 ] mean value: 0.8367016437668612 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.17 MCC on Training: 0.81 Running classifier: 4 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.00979877 0.01045513 0.01082087 0.01038527 0.01219773 0.00928116 0.00914526 0.00927854 0.00921845 0.00916553] mean value: 0.00997467041015625 key: score_time value: [0.00952768 0.00971723 0.00958252 0.00937033 0.01101089 0.00869083 0.00855565 0.00847483 0.00865817 0.00859499] mean value: 0.009218311309814453 key: test_mcc value: [0.94280904 0.94280904 0.73854895 0.88852332 0.94280904 0.83666003 0.78679579 0.69156407 0.68599434 0.47162111] mean value: 0.7928134730784403 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.97142857 0.97142857 0.87179487 0.94444444 0.97142857 0.91891892 0.89473684 0.85 0.84210526 0.76923077] mean value: 0.9005516823937878 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.94444444 0.94444444 0.77272727 0.89473684 0.94444444 0.85 0.80952381 0.73913043 0.72727273 0.68181818] mean value: 0.8308542601563197 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.88235294] mean value: 0.9882352941176471 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.97058824 0.97058824 0.85294118 0.94117647 0.97058824 0.91176471 0.88235294 0.82352941 0.81818182 0.72727273] mean value: 0.886898395721925 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.97058824 0.97058824 0.85294118 0.94117647 0.97058824 0.91176471 0.88235294 0.82352941 0.82352941 0.72242647] mean value: 0.8869485294117647 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.94444444 0.94444444 0.77272727 0.89473684 0.94444444 0.85 0.80952381 0.73913043 0.72727273 0.625 ] mean value: 0.8251724419745013 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.06 MCC on Training: 0.79 Running classifier: 5 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.10064697 0.11114359 0.11072612 0.10682464 0.10616851 0.11270022 0.10741401 0.10949302 0.1080184 0.10834241] mean value: 0.10814778804779053 key: score_time value: [0.01751614 0.01995921 0.0176003 0.01939297 0.01939392 0.01937819 0.01901865 0.01925159 0.018929 0.0191071 ] mean value: 0.018954706192016602 key: test_mcc value: [0.83666003 0.94280904 0.88852332 0.94280904 0.88852332 0.94280904 0.88852332 0.83666003 0.83276554 0.75735294] mean value: 0.8757435613350288 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.91891892 0.97142857 0.94444444 0.97142857 0.94444444 0.97142857 0.94444444 0.91891892 0.91428571 0.88235294] mean value: 0.9382095540919071 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.85 0.94444444 0.89473684 0.94444444 0.89473684 0.94444444 0.89473684 0.85 0.84210526 0.88235294] mean value: 0.8942002063983487 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.88235294] mean value: 0.9882352941176471 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.91176471 0.97058824 0.94117647 0.97058824 0.94117647 0.97058824 0.94117647 0.91176471 0.90909091 0.87878788] mean value: 0.9346702317290552 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.91176471 0.97058824 0.94117647 0.97058824 0.94117647 0.97058824 0.94117647 0.91176471 0.91176471 0.87867647] mean value: 0.9349264705882353 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.85 0.94444444 0.89473684 0.94444444 0.89473684 0.94444444 0.89473684 0.85 0.84210526 0.78947368] mean value: 0.8849122807017542 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.02 MCC on Training: 0.88 Running classifier: 6 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.43160248 0.42581844 0.42961097 0.4267993 0.43083239 0.44638181 0.43575931 0.46122932 0.45211387 0.43634796] mean value: 0.4376495838165283 key: score_time value: [0.00916576 0.00925207 0.00923896 0.00918102 0.00916624 0.01008844 0.00976372 0.00972533 0.00937891 0.01316142] mean value: 0.009812188148498536 key: test_mcc value: [0.94280904 0.88852332 0.83666003 0.94280904 0.83666003 1. 0.78679579 0.83666003 0.88561489 0.69742172] mean value: 0.8653953879777356 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.97142857 0.94444444 0.91891892 0.97142857 0.91891892 1. 0.89473684 0.91891892 0.94117647 0.85714286] mean value: 0.9337114513894701 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.94444444 0.89473684 0.85 0.94444444 0.85 1. 0.80952381 0.85 0.88888889 0.83333333] mean value: 0.8865371762740184 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.88235294] mean value: 0.9882352941176471 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.97058824 0.94117647 0.91176471 0.97058824 0.91176471 1. 0.88235294 0.91176471 0.93939394 0.84848485] mean value: 0.9287878787878787 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.97058824 0.94117647 0.91176471 0.97058824 0.91176471 1. 0.88235294 0.91176471 0.94117647 0.84742647] mean value: 0.9288602941176471 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.94444444 0.89473684 0.85 0.94444444 0.85 1. 0.80952381 0.85 0.88888889 0.75 ] mean value: 0.878203842940685 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.16 MCC on Training: 0.87 Running classifier: 7 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.00889444 0.00883698 0.00987315 0.00880814 0.00884581 0.00893903 0.00887346 0.00886536 0.00877404 0.00902772] mean value: 0.008973813056945801 key: score_time value: [0.00844479 0.0084362 0.00873637 0.00846124 0.00845838 0.00854564 0.00843215 0.00846529 0.00846624 0.0084734 ] mean value: 0.008491969108581543 key: test_mcc value: [0.5976143 0.17770466 0.41464421 0.53311399 0.41464421 0.65158377 0.23570226 0.17647059 0.17149859 0.45588235] mean value: 0.3828858939090333 key: train_mcc value: [0.54022243 0.49343173 0.46760138 0.51605844 0.49819867 0.51861886 0.54433566 0.46711537 0.47726291 0.56352357] mean value: 0.5086369024563571 key: test_fscore value: [0.81081081 0.61111111 0.6875 0.77777778 0.6875 0.83333333 0.62857143 0.58823529 0.63157895 0.72727273] mean value: 0.6983691430363257 key: train_fscore value: [0.77564103 0.74754098 0.73954984 0.77477477 0.76307692 0.77300613 0.7880597 0.73442623 0.75 0.78996865] mean value: 0.7636044264335253 key: test_precision value: [0.75 0.57894737 0.73333333 0.73684211 0.73333333 0.78947368 0.61111111 0.58823529 0.54545455 0.75 ] mean value: 0.6816730775244707 key: train_precision value: [0.75625 0.74509804 0.72327044 0.71270718 0.71676301 0.72413793 0.72131148 0.73202614 0.71856287 0.75449102] mean value: 0.7304618110018785 key: test_recall value: [0.88235294 0.64705882 0.64705882 0.82352941 0.64705882 0.88235294 0.64705882 0.58823529 0.75 0.70588235] mean value: 0.7220588235294118 key: train_recall value: [0.79605263 0.75 0.75657895 0.84868421 0.81578947 0.82894737 0.86842105 0.73684211 0.78431373 0.82894737] mean value: 0.8014576883384933 key: test_accuracy value: [0.79411765 0.58823529 0.70588235 0.76470588 0.70588235 0.82352941 0.61764706 0.58823529 0.57575758 0.72727273] mean value: 0.6891265597147951 key: train_accuracy value: [0.76973684 0.74671053 0.73355263 0.75328947 0.74671053 0.75657895 0.76644737 0.73355263 0.73770492 0.78032787] mean value: 0.7524611734253667 key: test_roc_auc value: [0.79411765 0.58823529 0.70588235 0.76470588 0.70588235 0.82352941 0.61764706 0.58823529 0.58088235 0.72794118] mean value: 0.6897058823529412 key: train_roc_auc value: [0.76973684 0.74671053 0.73355263 0.75328947 0.74671053 0.75657895 0.76644737 0.73355263 0.7375516 0.78048676] mean value: 0.7524617303061575 key: test_jcc value: [0.68181818 0.44 0.52380952 0.63636364 0.52380952 0.71428571 0.45833333 0.41666667 0.46153846 0.57142857] mean value: 0.5428053613053614 key: train_jcc value: [0.63350785 0.59685864 0.58673469 0.63235294 0.61691542 0.63 0.65024631 0.58031088 0.6 0.65284974] mean value: 0.6179776477266568 MCC on Blind test: -0.06 MCC on Training: 0.38 Running classifier: 8 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.04622698 0.08372498 0.09779835 0.11626935 0.0769794 0.09999418 0.09253001 0.07889533 0.04701853 0.06552267] mean value: 0.08049597740173339 key: score_time value: [0.01392269 0.02159524 0.02662206 0.0177629 0.01333427 0.02471828 0.02090764 0.01313829 0.01317787 0.02071214] mean value: 0.018589138984680176 key: test_mcc value: [0.78679579 0.83666003 0.83666003 0.88852332 0.88852332 0.83666003 0.83666003 0.64549722 0.7333588 0.83103851] mean value: 0.8120377060803227 key: train_mcc value: [0.96762892 0.96127552 0.96127552 0.96127552 0.95496057 0.96127552 0.96762892 0.95496057 0.96139484 0.95511121] mean value: 0.9606787131627254 key: test_fscore value: [0.89473684 0.91891892 0.91891892 0.94444444 0.94444444 0.91891892 0.91891892 0.82926829 0.86486486 0.91891892] mean value: 0.9072353483136538 key: train_fscore value: [0.98381877 0.98064516 0.98064516 0.98064516 0.97749196 0.98064516 0.98381877 0.97749196 0.98076923 0.97749196] mean value: 0.9803463300627968 key: test_precision value: [0.80952381 0.85 0.85 0.89473684 0.89473684 0.85 0.85 0.70833333 0.76190476 0.85 ] mean value: 0.8319235588972431 key: train_precision value: [0.96815287 0.96202532 0.96202532 0.96202532 0.95597484 0.96202532 0.96815287 0.95597484 0.96226415 0.95597484] mean value: 0.9614595677552144 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.88235294 0.91176471 0.91176471 0.94117647 0.94117647 0.91176471 0.91176471 0.79411765 0.84848485 0.90909091] mean value: 0.8963458110516932 key: train_accuracy value: [0.98355263 0.98026316 0.98026316 0.98026316 0.97697368 0.98026316 0.98355263 0.97697368 0.98032787 0.97704918] mean value: 0.9799482312338222 key: test_roc_auc value: [0.88235294 0.91176471 0.91176471 0.94117647 0.94117647 0.91176471 0.91176471 0.79411765 0.85294118 0.90625 ] mean value: 0.8965073529411764 key: train_roc_auc value: [0.98355263 0.98026316 0.98026316 0.98026316 0.97697368 0.98026316 0.98355263 0.97697368 0.98026316 0.97712418] mean value: 0.9799492604059168 key: test_jcc value: [0.80952381 0.85 0.85 0.89473684 0.89473684 0.85 0.85 0.70833333 0.76190476 0.85 ] mean value: 0.8319235588972431 key: train_jcc value: [0.96815287 0.96202532 0.96202532 0.96202532 0.95597484 0.96202532 0.96815287 0.95597484 0.96226415 0.95597484] mean value: 0.9614595677552144 MCC on Blind test: -0.08 MCC on Training: 0.81 Running classifier: 9 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.01199412 0.01027751 0.01018143 0.0102458 0.01004267 0.00917006 0.01069236 0.01149678 0.01058936 0.01029897] mean value: 0.010498905181884765 key: score_time value: [0.05478621 0.01434851 0.01448607 0.01271057 0.01256919 0.01250792 0.01954556 0.01717067 0.0132513 0.01600289] mean value: 0.01873788833618164 key: test_mcc value: [0.71713717 0.42365927 0.56582515 0.56582515 0.53311399 0.58925565 0.47140452 0.35856858 0.35522067 0.34299717] mean value: 0.49230073251470463 key: train_mcc value: [0.72577826 0.74967137 0.73133412 0.73690714 0.77506566 0.68282221 0.72114805 0.71335602 0.73527645 0.74130742] mean value: 0.7312666705328752 key: test_fscore value: [0.86486486 0.73684211 0.8 0.8 0.77777778 0.8 0.74285714 0.7027027 0.7027027 0.71794872] mean value: 0.7645696014117066 key: train_fscore value: [0.86803519 0.87951807 0.87058824 0.87315634 0.89020772 0.84955752 0.86646884 0.86309524 0.87315634 0.87537994] mean value: 0.8709163439857251 key: test_precision value: [0.8 0.66666667 0.69565217 0.69565217 0.73684211 0.77777778 0.72222222 0.65 0.61904762 0.63636364] mean value: 0.7000224375167168 key: train_precision value: [0.78306878 0.81111111 0.78723404 0.79144385 0.81081081 0.77005348 0.78918919 0.78804348 0.79569892 0.81355932] mean value: 0.7940212987962246 key: test_recall value: [0.94117647 0.82352941 0.94117647 0.94117647 0.82352941 0.82352941 0.76470588 0.76470588 0.8125 0.82352941] mean value: 0.8459558823529412 key: train_recall value: [0.97368421 0.96052632 0.97368421 0.97368421 0.98684211 0.94736842 0.96052632 0.95394737 0.96732026 0.94736842] mean value: 0.9644951840385276 key: test_accuracy value: [0.85294118 0.70588235 0.76470588 0.76470588 0.76470588 0.79411765 0.73529412 0.67647059 0.66666667 0.66666667] mean value: 0.7392156862745098 key: train_accuracy value: [0.85197368 0.86842105 0.85526316 0.85855263 0.87828947 0.83223684 0.85197368 0.84868421 0.85901639 0.86557377] mean value: 0.8569984900776533 key: test_roc_auc value: [0.85294118 0.70588235 0.76470588 0.76470588 0.76470588 0.79411765 0.73529412 0.67647059 0.67095588 0.66176471] mean value: 0.7391544117647059 key: train_roc_auc value: [0.85197368 0.86842105 0.85526316 0.85855263 0.87828947 0.83223684 0.85197368 0.84868421 0.85866013 0.86584107] mean value: 0.8569895940832474 key: test_jcc value: [0.76190476 0.58333333 0.66666667 0.66666667 0.63636364 0.66666667 0.59090909 0.54166667 0.54166667 0.56 ] mean value: 0.6215844155844156 key: train_jcc value: [0.76683938 0.78494624 0.77083333 0.77486911 0.80213904 0.73846154 0.76439791 0.7591623 0.77486911 0.77837838] mean value: 0.7714896331723258 MCC on Blind test: -0.04 MCC on Training: 0.49 Running classifier: 10 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.03473806 0.06413674 0.03305054 0.03353834 0.03425193 0.0341835 0.03371 0.05545449 0.05507493 0.04306841] mean value: 0.04212069511413574 key: score_time value: [0.02053285 0.01193166 0.01199627 0.01196361 0.01198816 0.01195765 0.0118773 0.02190852 0.02237725 0.01199794] mean value: 0.014853119850158691 key: test_mcc value: [0.51639778 0.77005354 0.77005354 0.66575029 0.73854895 0.88235294 0.78679579 0.64549722 0.7333588 0.63944497] mean value: 0.7148253820742334 key: train_mcc value: [0.9544639 0.93550714 0.94129888 0.92861683 0.95496057 0.92233098 0.9486833 0.96127552 0.96772965 0.96140461] mean value: 0.94762713904332 key: test_fscore value: [0.7804878 0.88888889 0.88888889 0.84210526 0.87179487 0.94117647 0.89473684 0.82926829 0.86486486 0.83333333] mean value: 0.8635545521183217 key: train_fscore value: [0.97734628 0.96794872 0.97087379 0.96463023 0.97749196 0.96153846 0.97435897 0.98064516 0.98392283 0.98064516] mean value: 0.9739401557228888 key: test_precision value: [0.66666667 0.84210526 0.84210526 0.76190476 0.77272727 0.94117647 0.80952381 0.70833333 0.76190476 0.78947368] mean value: 0.7895921287175157 key: train_precision value: [0.96178344 0.94375 0.95541401 0.94339623 0.95597484 0.9375 0.95 0.96202532 0.96835443 0.96202532] mean value: 0.9540223584702829 key: test_recall value: [0.94117647 0.94117647 0.94117647 0.94117647 1. 0.94117647 1. 1. 1. 0.88235294] mean value: 0.9588235294117649 key: train_recall value: [0.99342105 0.99342105 0.98684211 0.98684211 1. 0.98684211 1. 1. 1. 1. ] mean value: 0.9947368421052631 key: test_accuracy value: [0.73529412 0.88235294 0.88235294 0.82352941 0.85294118 0.94117647 0.88235294 0.79411765 0.84848485 0.81818182] mean value: 0.8460784313725489 key: train_accuracy value: [0.97697368 0.96710526 0.97039474 0.96381579 0.97697368 0.96052632 0.97368421 0.98026316 0.98360656 0.98032787] mean value: 0.9733671268334773 key: test_roc_auc value: [0.73529412 0.88235294 0.88235294 0.82352941 0.85294118 0.94117647 0.88235294 0.79411765 0.85294118 0.81617647] mean value: 0.8463235294117647 key: train_roc_auc value: [0.97697368 0.96710526 0.97039474 0.96381579 0.97697368 0.96052632 0.97368421 0.98026316 0.98355263 0.98039216] mean value: 0.9733681630546955 key: test_jcc value: [0.64 0.8 0.8 0.72727273 0.77272727 0.88888889 0.80952381 0.70833333 0.76190476 0.71428571] mean value: 0.7622936507936509 key: train_jcc value: [0.9556962 0.9378882 0.94339623 0.93167702 0.95597484 0.92592593 0.95 0.96202532 0.96835443 0.96202532] mean value: 0.9492963478322405 MCC on Blind test: -0.03 MCC on Training: 0.71 Running classifier: 11 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.04974842 0.03734469 0.03741765 0.0367322 0.03805566 0.03690672 0.03844476 0.06246209 0.05121088 0.05709696] mean value: 0.04454200267791748 key: score_time value: [0.01234603 0.01221681 0.01231885 0.01241469 0.01253271 0.01254225 0.01242208 0.01226234 0.01262403 0.01269531] mean value: 0.01243751049041748 key: test_mcc value: [0.5976143 0.70710678 0.54470478 0.58925565 0.52941176 0.83666003 0.53311399 0.73854895 0.64207079 0.64207079] mean value: 0.6360557827376732 key: train_mcc value: [0.76315789 0.73709738 0.79118777 0.79118777 0.80936818 0.77108971 0.83699481 0.76988681 0.77729509 0.7908009 ] mean value: 0.7838066317216661 key: test_fscore value: [0.81081081 0.85714286 0.78947368 0.8 0.76470588 0.91891892 0.77777778 0.87179487 0.82352941 0.8125 ] mean value: 0.8226654214773408 key: train_fscore value: [0.88157895 0.87012987 0.89808917 0.89808917 0.90553746 0.88817891 0.9201278 0.88599349 0.89032258 0.89677419] mean value: 0.8934821589531466 key: test_precision value: [0.75 0.83333333 0.71428571 0.77777778 0.76470588 0.85 0.73684211 0.77272727 0.77777778 0.86666667] mean value: 0.784411653018464 key: train_precision value: [0.88157895 0.85897436 0.87037037 0.87037037 0.89677419 0.86335404 0.89440994 0.87741935 0.87898089 0.87974684] mean value: 0.8771979297788679 key: test_recall value: [0.88235294 0.88235294 0.88235294 0.82352941 0.76470588 1. 0.82352941 1. 0.875 0.76470588] mean value: 0.8698529411764706 key: train_recall value: [0.88157895 0.88157895 0.92763158 0.92763158 0.91447368 0.91447368 0.94736842 0.89473684 0.90196078 0.91447368] mean value: 0.9105908152734777 key: test_accuracy value: [0.79411765 0.85294118 0.76470588 0.79411765 0.76470588 0.91176471 0.76470588 0.85294118 0.81818182 0.81818182] mean value: 0.8136363636363637 key: train_accuracy value: [0.88157895 0.86842105 0.89473684 0.89473684 0.90460526 0.88486842 0.91776316 0.88486842 0.88852459 0.89508197] mean value: 0.891518550474547 key: test_roc_auc value: [0.79411765 0.85294118 0.76470588 0.79411765 0.76470588 0.91176471 0.76470588 0.85294118 0.81985294 0.81985294] mean value: 0.813970588235294 key: train_roc_auc value: [0.88157895 0.86842105 0.89473684 0.89473684 0.90460526 0.88486842 0.91776316 0.88486842 0.88848039 0.89514534] mean value: 0.8915204678362574 key: test_jcc value: [0.68181818 0.75 0.65217391 0.66666667 0.61904762 0.85 0.63636364 0.77272727 0.7 0.68421053] mean value: 0.7013007815982644 key: train_jcc value: [0.78823529 0.77011494 0.8150289 0.8150289 0.82738095 0.79885057 0.85207101 0.79532164 0.80232558 0.8128655 ] mean value: 0.8077223289023617 MCC on Blind test: 0.01 MCC on Training: 0.64 Running classifier: 12 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(cv=3, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(cv=3, random_state=42))]) key: fit_time value: [0.47088957 0.48974657 0.48263764 0.64673495 0.50060296 0.50281286 0.47460079 0.4915688 0.52541208 0.49458122] mean value: 0.5079587459564209 key: score_time value: [0.01212025 0.01203656 0.01226282 0.01206827 0.01201034 0.01221347 0.01220274 0.01205254 0.01199007 0.01196551] mean value: 0.012092256546020507 key: test_mcc value: [0.78679579 1. 0.64549722 0.83666003 0.78679579 0.94280904 0.83666003 0.78679579 0.7333588 0.75735294] mean value: 0.8112725435225485 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.89473684 1. 0.82926829 0.91891892 0.89473684 0.97142857 0.91891892 0.89473684 0.86486486 0.88235294] mean value: 0.9069963034306461 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.80952381 1. 0.70833333 0.85 0.80952381 0.94444444 0.85 0.80952381 0.76190476 0.88235294] mean value: 0.8425606909430439 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.88235294] mean value: 0.9882352941176471 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.88235294 1. 0.79411765 0.91176471 0.88235294 0.97058824 0.91176471 0.88235294 0.84848485 0.87878788] mean value: 0.8962566844919785 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.88235294 1. 0.79411765 0.91176471 0.88235294 0.97058824 0.91176471 0.88235294 0.85294118 0.87867647] mean value: 0.8966911764705883 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.80952381 1. 0.70833333 0.85 0.80952381 0.94444444 0.85 0.80952381 0.76190476 0.78947368] mean value: 0.8332727652464496 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.0 MCC on Training: 0.81 Running classifier: 13 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [1.47393632 1.5891304 1.40466785 1.39706254 1.40532374 1.27637243 1.36967158 1.37888503 1.56799555 1.48899674] mean value: 1.4352042198181152 key: score_time value: [0.01429367 0.01442671 0.01413321 0.01241517 0.01285005 0.01306725 0.01312113 0.01306319 0.01250148 0.0125463 ] mean value: 0.013241815567016601 key: test_mcc value: [0.78679579 0.94280904 0.73854895 0.88852332 0.78679579 0.94280904 0.73854895 0.78679579 0.78215389 0.75735294] mean value: 0.81511334990146 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.89473684 0.97142857 0.87179487 0.94444444 0.89473684 0.97142857 0.87179487 0.89473684 0.88888889 0.88235294] mean value: 0.908634368727248 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.80952381 0.94444444 0.77272727 0.89473684 0.80952381 0.94444444 0.77272727 0.80952381 0.8 0.88235294] mean value: 0.8440004646196597 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.88235294] mean value: 0.9882352941176471 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.88235294 0.97058824 0.85294118 0.94117647 0.88235294 0.97058824 0.85294118 0.88235294 0.87878788 0.87878788] mean value: 0.8992869875222815 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.88235294 0.97058824 0.85294118 0.94117647 0.88235294 0.97058824 0.85294118 0.88235294 0.88235294 0.87867647] mean value: 0.8996323529411765 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.80952381 0.94444444 0.77272727 0.89473684 0.80952381 0.94444444 0.77272727 0.80952381 0.8 0.78947368] mean value: 0.8347125389230652 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.08 MCC on Training: 0.82 Running classifier: 14 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.0129323 0.01298022 0.00967455 0.00937486 0.00908685 0.00918198 0.00912213 0.00903177 0.00941968 0.00908828] mean value: 0.009989261627197266 key: score_time value: [0.01175714 0.01057267 0.00900412 0.00895619 0.00863934 0.00844359 0.00858355 0.00857115 0.00849795 0.00866866] mean value: 0.009169435501098633 key: test_mcc value: [0.47140452 0.53311399 0.54470478 0.35355339 0.29617444 0.70710678 0.11952286 0.47140452 0.14870813 0.46471292] mean value: 0.41104063309224215 key: train_mcc value: [0.4366363 0.42771489 0.47384831 0.42786313 0.46760138 0.4211985 0.46719627 0.44087535 0.42962199 0.44279515] mean value: 0.44353512826305347 key: test_fscore value: [0.74285714 0.77777778 0.73333333 0.68571429 0.625 0.85714286 0.51612903 0.74285714 0.53333333 0.70967742] mean value: 0.6923822324628777 key: train_fscore value: [0.70138889 0.71096346 0.73333333 0.7090301 0.72727273 0.70666667 0.73089701 0.71760797 0.71287129 0.71571906] mean value: 0.7165750505708133 key: test_precision value: [0.72222222 0.73684211 0.84615385 0.66666667 0.66666667 0.83333333 0.57142857 0.72222222 0.57142857 0.78571429] mean value: 0.7122678491099543 key: train_precision value: [0.74264706 0.71812081 0.74324324 0.72108844 0.74482759 0.71621622 0.73825503 0.72483221 0.72 0.72789116] mean value: 0.7297121750017894 key: test_recall value: [0.76470588 0.82352941 0.64705882 0.70588235 0.58823529 0.88235294 0.47058824 0.76470588 0.5 0.64705882] mean value: 0.6794117647058824 key: train_recall value: [0.66447368 0.70394737 0.72368421 0.69736842 0.71052632 0.69736842 0.72368421 0.71052632 0.70588235 0.70394737] mean value: 0.704140866873065 key: test_accuracy value: [0.73529412 0.76470588 0.76470588 0.67647059 0.64705882 0.85294118 0.55882353 0.73529412 0.57575758 0.72727273] mean value: 0.7038324420677362 key: train_accuracy value: [0.71710526 0.71381579 0.73684211 0.71381579 0.73355263 0.71052632 0.73355263 0.72039474 0.7147541 0.72131148] mean value: 0.7215670836928385 key: test_roc_auc value: [0.73529412 0.76470588 0.76470588 0.67647059 0.64705882 0.85294118 0.55882353 0.73529412 0.57352941 0.72977941] mean value: 0.703860294117647 key: train_roc_auc value: [0.71710526 0.71381579 0.73684211 0.71381579 0.73355263 0.71052632 0.73355263 0.72039474 0.71478328 0.72125473] mean value: 0.7215643274853801 key: test_jcc value: [0.59090909 0.63636364 0.57894737 0.52173913 0.45454545 0.75 0.34782609 0.59090909 0.36363636 0.55 ] mean value: 0.5384876222175994 key: train_jcc value: [0.54010695 0.55154639 0.57894737 0.5492228 0.57142857 0.54639175 0.57591623 0.55958549 0.55384615 0.55729167] mean value: 0.5584283377085932 MCC on Blind test: -0.11 MCC on Training: 0.41 Running classifier: 15 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.01091456 0.00948477 0.0096457 0.00991011 0.00984836 0.00954223 0.00940657 0.00979161 0.00960279 0.00972033] mean value: 0.009786701202392578 key: score_time value: [0.00951672 0.00908923 0.00965738 0.00911117 0.01305985 0.00896645 0.00859213 0.00868034 0.00877547 0.00888467] mean value: 0.009433341026306153 key: test_mcc value: [0.35355339 0.53311399 0.70710678 0.53311399 0.41464421 0.61545745 0.35355339 0.54470478 0.33455882 0.57720588] mean value: 0.4967012696951764 key: train_mcc value: [0.66157016 0.59648313 0.65200756 0.63245553 0.62501353 0.53089528 0.63157895 0.62035732 0.66632263 0.65352757] mean value: 0.6270211649554502 key: test_fscore value: [0.68571429 0.75 0.84848485 0.75 0.6875 0.75862069 0.66666667 0.78947368 0.66666667 0.78787879] mean value: 0.7391005629276954 key: train_fscore value: [0.81944444 0.77090909 0.82154882 0.81081081 0.81311475 0.73260073 0.81578947 0.80136986 0.82943144 0.82033898] mean value: 0.8035358412288108 key: test_precision value: [0.66666667 0.8 0.875 0.8 0.73333333 0.91666667 0.6875 0.71428571 0.64705882 0.8125 ] mean value: 0.7653011204481793 key: train_precision value: [0.86764706 0.86178862 0.84137931 0.83333333 0.81045752 0.82644628 0.81578947 0.83571429 0.84931507 0.84615385] mean value: 0.8388024791764968 key: test_recall value: [0.70588235 0.70588235 0.82352941 0.70588235 0.64705882 0.64705882 0.64705882 0.88235294 0.6875 0.76470588] mean value: 0.7216911764705882 key: train_recall value: [0.77631579 0.69736842 0.80263158 0.78947368 0.81578947 0.65789474 0.81578947 0.76973684 0.81045752 0.79605263] mean value: 0.7731510147918816 key: test_accuracy value: [0.67647059 0.76470588 0.85294118 0.76470588 0.70588235 0.79411765 0.67647059 0.76470588 0.66666667 0.78787879] mean value: 0.7454545454545455 key: train_accuracy value: [0.82894737 0.79276316 0.82565789 0.81578947 0.8125 0.75986842 0.81578947 0.80921053 0.83278689 0.82622951] mean value: 0.8119542709232096 key: test_roc_auc value: [0.67647059 0.76470588 0.85294118 0.76470588 0.70588235 0.79411765 0.67647059 0.76470588 0.66727941 0.78860294] mean value: 0.7455882352941176 key: train_roc_auc value: [0.82894737 0.79276316 0.82565789 0.81578947 0.8125 0.75986842 0.81578947 0.80921053 0.83286034 0.82613089] mean value: 0.8119517543859651 key: test_jcc value: [0.52173913 0.6 0.73684211 0.6 0.52380952 0.61111111 0.5 0.65217391 0.5 0.65 ] mean value: 0.5895675783662053 key: train_jcc value: [0.69411765 0.62721893 0.69714286 0.68181818 0.68508287 0.57803468 0.68888889 0.66857143 0.70857143 0.6954023 ] mean value: 0.6724849220822527 MCC on Blind test: -0.16 MCC on Training: 0.5 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.01400113 0.02024388 0.01866746 0.01594639 0.01741314 0.01965547 0.02110958 0.01688814 0.01624131 0.02125883] mean value: 0.01814253330230713 key: score_time value: [0.00935411 0.01179767 0.01179266 0.01175761 0.01175213 0.01180959 0.01194143 0.01174712 0.01186562 0.01182652] mean value: 0.01156444549560547 key: test_mcc value: [0.25819889 0.77005354 0.61545745 0.5976143 0.38729833 0.88852332 0.65158377 0.4152274 0.54879547 0.63944497] mean value: 0.5772197450543258 key: train_mcc value: [0.6475021 0.82453037 0.87212823 0.76880521 0.38131571 0.85284713 0.86909842 0.29277002 0.71117419 0.8193943 ] mean value: 0.703956567930743 key: test_fscore value: [0.51851852 0.875 0.82051282 0.81081081 0.59259259 0.9375 0.8125 0.45454545 0.7804878 0.83333333] mean value: 0.7435801335191579 key: train_fscore value: [0.76 0.90784983 0.93710692 0.88821752 0.42268041 0.92041522 0.93288591 0.27272727 0.85955056 0.91017964] mean value: 0.7811613288817626 key: test_precision value: [0.7 0.93333333 0.72727273 0.75 0.8 1. 0.86666667 1. 0.64 0.78947368] mean value: 0.8206746411483253 key: train_precision value: [0.96938776 0.94326241 0.89759036 0.82122905 0.97619048 0.97080292 0.95205479 1. 0.75369458 0.83516484] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) mean value: 0.9119377185039348 key: test_recall value: [0.41176471 0.82352941 0.94117647 0.88235294 0.47058824 0.88235294 0.76470588 0.29411765 1. 0.88235294] mean value: 0.7352941176470589 key: train_recall value: [0.625 0.875 0.98026316 0.96710526 0.26973684 0.875 0.91447368 0.15789474 1. 1. ] mean value: 0.7664473684210525 key: test_accuracy value: [0.61764706 0.88235294 0.79411765 0.79411765 0.67647059 0.94117647 0.82352941 0.64705882 0.72727273 0.81818182] mean value: 0.772192513368984 key: train_accuracy value: [0.80263158 0.91118421 0.93421053 0.87828947 0.63157895 0.92434211 0.93421053 0.57894737 0.83606557 0.90163934] mean value: 0.8333099654874891 key: test_roc_auc value: [0.61764706 0.88235294 0.79411765 0.79411765 0.67647059 0.94117647 0.82352941 0.64705882 0.73529412 0.81617647] mean value: 0.7727941176470587 key: train_roc_auc value: [0.80263158 0.91118421 0.93421053 0.87828947 0.63157895 0.92434211 0.93421053 0.57894737 0.83552632 0.90196078] mean value: 0.8332881836945305 key: test_jcc value: [0.35 0.77777778 0.69565217 0.68181818 0.42105263 0.88235294 0.68421053 0.29411765 0.64 0.71428571] mean value: 0.6141267593924747 key: train_jcc value: [0.61290323 0.83125 0.8816568 0.79891304 0.26797386 0.8525641 0.87421384 0.15789474 0.75369458 0.83516484] mean value: 0.686622902255741 MCC on Blind test: -0.12 MCC on Training: 0.58 Running classifier: 17 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.02404714 0.0243485 0.02428913 0.02510905 0.0243814 0.02542853 0.02359772 0.0237236 0.02466941 0.02439046] mean value: 0.024398493766784667 key: score_time value: [0.01234961 0.0124042 0.01233482 0.01237607 0.01232862 0.01241064 0.0123775 0.01239562 0.01314163 0.012362 ] mean value: 0.01244807243347168 key: test_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 0.94117647 0.88561489] mean value: 0.982679135612833 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 0.96969697 0.9375 ] mean value: 0.9907196969696969 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 0.94117647 1. ] mean value: 0.9941176470588236 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.88235294] mean value: 0.9882352941176471 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 0.96969697 0.93939394] mean value: 0.9909090909090909 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 0.97058824 0.94117647] mean value: 0.9911764705882353 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 0.94117647 0.88235294] mean value: 0.9823529411764707 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.0 MCC on Training: 0.98 Running classifier: 18 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42))]) key: fit_time value: [0.61342001 0.69472289 0.62804198 0.6471889 0.67946577 0.66296935 0.66678095 0.65193343 0.59468579 0.71978855] mean value: 0.6558997631072998 key: score_time value: [0.1854012 0.19020963 0.16943336 0.19882059 0.16746783 0.18957543 0.17446709 0.19675732 0.16393733 0.21294188] mean value: 0.18490116596221923 key: test_mcc value: [0.88852332 0.94280904 0.88852332 0.94280904 0.88852332 1. 0.83666003 0.73854895 0.83276554 0.75735294] mean value: 0.8716515491110147 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.94444444 0.97142857 0.94444444 0.97142857 0.94444444 1. 0.91891892 0.87179487 0.91428571 0.88235294] mean value: 0.9363542922366452 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.89473684 0.94444444 0.89473684 0.94444444 0.89473684 1. 0.85 0.77272727 0.84210526 0.88235294] mean value: 0.8920284892266317 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.88235294] mean value: 0.9882352941176471 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.94117647 0.97058824 0.94117647 0.97058824 0.94117647 1. 0.91176471 0.85294118 0.90909091 0.87878788] mean value: 0.9317290552584669 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.94117647 0.97058824 0.94117647 0.97058824 0.94117647 1. 0.91176471 0.85294118 0.91176471 0.87867647] mean value: 0.9319852941176471 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.89473684 0.94444444 0.89473684 0.94444444 0.89473684 1. 0.85 0.77272727 0.84210526 0.78947368] mean value: 0.8827405635300372 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.06 MCC on Training: 0.87 Running classifier: 19 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea...age_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42))]) key: fit_time value: [0.91779685 0.94348431 0.96264386 0.95950675 0.9686687 0.98237991 1.01766539 0.9825449 0.98976827 0.9663403 ] mean value: 0.9690799236297607 key: score_time value: [0.23324203 0.18031478 0.22315073 0.18287873 0.25690937 0.17122674 0.22323728 0.14594388 0.26834369 0.17882085] mean value: 0.2064068078994751 key: test_mcc value: [0.82495791 0.82495791 0.78679579 0.66575029 0.76470588 1. 0.83666003 0.49236596 0.88561489 0.69742172] mean value: 0.7779230381881004 key: train_mcc value: [0.96729368 0.94810737 0.96762892 0.94769663 0.96729368 0.95496057 0.96762892 0.96085908 0.95509799 0.98697054] mean value: 0.9623537388597271 key: test_fscore value: [0.91428571 0.91428571 0.89473684 0.84210526 0.88235294 1. 0.91891892 0.76923077 0.94117647 0.85714286] mean value: 0.8934235490891839 key: train_fscore value: [0.98371336 0.97419355 0.98381877 0.97402597 0.98371336 0.97749196 0.98381877 0.98051948 0.97763578 0.99346405] mean value: 0.9812395049933322 key: test_precision value: [0.88888889 0.88888889 0.80952381 0.76190476 0.88235294 1. 0.85 0.68181818 0.88888889 0.83333333] mean value: 0.8485599694423224 key: train_precision value: [0.97419355 0.9556962 0.96815287 0.96153846 0.97419355 0.95597484 0.96815287 0.96794872 0.95625 0.98701299] mean value: 0.9669114041057378 key: test_recall value: [0.94117647 0.94117647 1. 0.94117647 0.88235294 1. 1. 0.88235294 1. 0.88235294] mean value: 0.9470588235294117 key: train_recall value: [0.99342105 0.99342105 1. 0.98684211 0.99342105 1. 1. 0.99342105 1. 1. ] mean value: 0.9960526315789474 key: test_accuracy value: [0.91176471 0.91176471 0.88235294 0.82352941 0.88235294 1. 0.91176471 0.73529412 0.93939394 0.84848485] mean value: 0.8846702317290551 key: train_accuracy value: [0.98355263 0.97368421 0.98355263 0.97368421 0.98355263 0.97697368 0.98355263 0.98026316 0.97704918 0.99344262] mean value: 0.9809307592752372 key: test_roc_auc value: [0.91176471 0.91176471 0.88235294 0.82352941 0.88235294 1. 0.91176471 0.73529412 0.94117647 0.84742647] mean value: 0.8847426470588236 key: train_roc_auc value: [0.98355263 0.97368421 0.98355263 0.97368421 0.98355263 0.97697368 0.98355263 0.98026316 0.97697368 0.99346405] mean value: 0.9809253525971793 key: test_jcc value: [0.84210526 0.84210526 0.80952381 0.72727273 0.78947368 1. 0.85 0.625 0.88888889 0.75 ] mean value: 0.8124369636211741 key: train_jcc value: [0.96794872 0.94968553 0.96815287 0.94936709 0.96794872 0.95597484 0.96815287 0.96178344 0.95625 0.98701299] mean value: 0.9632277060851031 MCC on Blind test: 0.08 MCC on Training: 0.78 Running classifier: 20 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.01577067 0.0220027 0.01466608 0.01647735 0.03517413 0.01572156 0.01462722 0.02800703 0.04973674 0.03647995] mean value: 0.024866342544555664 key: score_time value: [0.01180959 0.01203394 0.01222587 0.01995707 0.0120728 0.01200509 0.01207471 0.02132702 0.01861405 0.0235045 ] mean value: 0.01556246280670166 key: test_mcc value: [0.71713717 0.82495791 0.61545745 0.65158377 0.76470588 0.94280904 0.41464421 0.6 0.71008133 0.63944497] mean value: 0.6880821742180357 key: train_mcc value: [0.92861683 0.90368528 0.90289991 0.90860289 0.91073734 0.91177042 0.90915483 0.93007025 0.91570585 0.9241952 ] mean value: 0.9145438810794279 key: test_fscore value: [0.86486486 0.91428571 0.82051282 0.83333333 0.88235294 0.97142857 0.72222222 0.80952381 0.85714286 0.83333333] mean value: 0.8509000467823998 key: train_fscore value: [0.96463023 0.95238095 0.95207668 0.95483871 0.9556962 0.95597484 0.95512821 0.96507937 0.95846645 0.96202532] mean value: 0.9576296950091381 key: test_precision value: [0.8 0.88888889 0.72727273 0.78947368 0.88235294 0.94444444 0.68421053 0.68 0.78947368 0.78947368] mean value: 0.79755905807299 key: train_precision value: [0.94339623 0.9202454 0.92546584 0.93670886 0.92073171 0.91566265 0.93125 0.93251534 0.9375 0.92682927] mean value: 0.9290305288092391 key: test_recall value: [0.94117647 0.94117647 0.94117647 0.88235294 0.88235294 1. 0.76470588 1. 0.9375 0.88235294] mean value: 0.917279411764706 key: train_recall value: [0.98684211 0.98684211 0.98026316 0.97368421 0.99342105 1. 0.98026316 1. 0.98039216 1. ] mean value: 0.9881707946336429 key: test_accuracy value: [0.85294118 0.91176471 0.79411765 0.82352941 0.88235294 0.97058824 0.70588235 0.76470588 0.84848485 0.81818182] mean value: 0.8372549019607843 key: train_accuracy value: [0.96381579 0.95065789 0.95065789 0.95394737 0.95394737 0.95394737 0.95394737 0.96381579 0.95737705 0.96065574] mean value: 0.9562769628990511 key: test_roc_auc value: [0.85294118 0.91176471 0.79411765 0.82352941 0.88235294 0.97058824 0.70588235 0.76470588 0.85110294 0.81617647] mean value: 0.8373161764705882 key: train_roc_auc value: [0.96381579 0.95065789 0.95065789 0.95394737 0.95394737 0.95394737 0.95394737 0.96381579 0.95730134 0.96078431] mean value: 0.9562822497420023 key: test_jcc value: [0.76190476 0.84210526 0.69565217 0.71428571 0.78947368 0.94444444 0.56521739 0.68 0.75 0.71428571] mean value: 0.7457369147506447 key: train_jcc value: [0.93167702 0.90909091 0.90853659 0.91358025 0.91515152 0.91566265 0.91411043 0.93251534 0.9202454 0.92682927] mean value: 0.9187399359694665 MCC on Blind test: -0.09 MCC on Training: 0.69 Running classifier: 21 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=3))]) key: fit_time value: [0.14279556 0.15015125 0.16179466 0.11629629 0.11289287 0.11274409 0.11403751 0.12125635 0.11322904 0.12390375] mean value: 0.12691013813018798 key: score_time value: [0.02368617 0.01837492 0.01398015 0.024369 0.02145195 0.01690459 0.01943016 0.0221293 0.02221584 0.01860714] mean value: 0.020114922523498537 key: test_mcc value: [0.73854895 0.82495791 0.66575029 0.71713717 0.78679579 0.88235294 0.73854895 0.6 0.71008133 0.69742172] mean value: 0.7361595045452429 key: train_mcc value: [0.96127552 0.90368528 0.94129888 0.92861683 0.96127552 0.93493921 0.94244297 0.96762892 0.91570585 0.94885552] mean value: 0.9405724513556294 key: test_fscore value: [0.87179487 0.91428571 0.84210526 0.86486486 0.89473684 0.94117647 0.87179487 0.80952381 0.85714286 0.85714286] mean value: 0.8724568422401241 key: train_fscore value: [0.98064516 0.95238095 0.97087379 0.96463023 0.98064516 0.96774194 0.97124601 0.98381877 0.95846645 0.97435897] mean value: 0.9704807426583031 key: test_precision value: [0.77272727 0.88888889 0.76190476 0.8 0.80952381 0.94117647 0.77272727 0.68 0.78947368 0.83333333] mean value: 0.80497554939041 key: train_precision value: [0.96202532 0.9202454 0.95541401 0.94339623 0.96202532 0.94936709 0.94409938 0.96815287 0.9375 0.95 ] mean value: 0.9492225604569967 key: test_recall value: [1. 0.94117647 0.94117647 0.94117647 1. 0.94117647 1. 1. 0.9375 0.88235294] mean value: 0.9584558823529413 key: train_recall value: [1. 0.98684211 0.98684211 0.98684211 1. 0.98684211 1. 1. 0.98039216 1. ] mean value: 0.9927760577915377 key: test_accuracy value: [0.85294118 0.91176471 0.82352941 0.85294118 0.88235294 0.94117647 0.85294118 0.76470588 0.84848485 0.84848485] mean value: 0.8579322638146168 key: train_accuracy value: [0.98026316 0.95065789 0.97039474 0.96381579 0.98026316 0.96710526 0.97039474 0.98355263 0.95737705 0.97377049] mean value: 0.9697594909404659 key: test_roc_auc value: [0.85294118 0.91176471 0.82352941 0.85294118 0.88235294 0.94117647 0.85294118 0.76470588 0.85110294 0.84742647] mean value: 0.8580882352941177 key: train_roc_auc value: [0.98026316 0.95065789 0.97039474 0.96381579 0.98026316 0.96710526 0.97039474 0.98355263 0.95730134 0.97385621] mean value: 0.9697604919160648 key: test_jcc value: [0.77272727 0.84210526 0.72727273 0.76190476 0.80952381 0.88888889 0.77272727 0.68 0.75 0.75 ] mean value: 0.7755149996202627 key: train_jcc value: [0.96202532 0.90909091 0.94339623 0.93167702 0.96202532 0.9375 0.94409938 0.96815287 0.9202454 0.95 ] mean value: 0.9428212430947968 MCC on Blind test: -0.05 MCC on Training: 0.74 Running classifier: 22 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.03304124 0.01714587 0.01656842 0.01622343 0.0172224 0.01719809 0.01617622 0.01623988 0.01689887 0.01874208] mean value: 0.018545651435852052 key: score_time value: [0.01153541 0.01112652 0.0119226 0.01115036 0.01054263 0.01157284 0.01114821 0.01133418 0.01174879 0.01080084] mean value: 0.01128823757171631 key: test_mcc value: [0.78679579 0.78679579 0.58925565 0.58925565 0.53311399 0.88852332 0.23904572 0.61545745 0.52029875 0.58739713] mean value: 0.613593924065522 key: train_mcc value: [0.750585 0.78427073 0.75856794 0.73786479 0.75657792 0.77800131 0.73040543 0.75104094 0.75136678 0.78446341] mean value: 0.7583144249528913 key: test_fscore value: [0.89473684 0.89473684 0.8 0.8 0.77777778 0.94444444 0.58064516 0.82051282 0.76470588 0.77419355] mean value: 0.8051753318975929 key: train_fscore value: [0.87741935 0.89456869 0.88253968 0.87179487 0.88271605 0.89171975 0.86644951 0.87820513 0.87820513 0.89389068] mean value: 0.8817508836926823 key: test_precision value: [0.80952381 0.80952381 0.77777778 0.77777778 0.73684211 0.89473684 0.64285714 0.72727273 0.72222222 0.85714286] mean value: 0.7755677071466545 key: train_precision value: [0.86075949 0.86956522 0.85276074 0.85 0.83139535 0.86419753 0.85806452 0.85625 0.86163522 0.87421384] mean value: 0.8578841899692723 key: test_recall value: [1. 1. 0.82352941 0.82352941 0.82352941 1. 0.52941176 0.94117647 0.8125 0.70588235] mean value: 0.8459558823529412 key: train_recall value: [0.89473684 0.92105263 0.91447368 0.89473684 0.94078947 0.92105263 0.875 0.90131579 0.89542484 0.91447368] mean value: 0.9073056415548676 key: test_accuracy value: [0.88235294 0.88235294 0.79411765 0.79411765 0.76470588 0.94117647 0.61764706 0.79411765 0.75757576 0.78787879] mean value: 0.8016042780748662 key: train_accuracy value: [0.875 0.89144737 0.87828947 0.86842105 0.875 0.88815789 0.86513158 0.875 0.87540984 0.89180328] mean value: 0.8783660483175151 key: test_roc_auc value: [0.88235294 0.88235294 0.79411765 0.79411765 0.76470588 0.94117647 0.61764706 0.79411765 0.75919118 0.79044118] mean value: 0.8020220588235294 key: train_roc_auc value: [0.875 0.89144737 0.87828947 0.86842105 0.875 0.88815789 0.86513158 0.875 0.875344 0.89187736] mean value: 0.8783668730650156 key: test_jcc value: [0.80952381 0.80952381 0.66666667 0.66666667 0.63636364 0.89473684 0.40909091 0.69565217 0.61904762 0.63157895] mean value: 0.6838851080269844 key: train_jcc value: [0.7816092 0.80924855 0.78977273 0.77272727 0.79005525 0.8045977 0.76436782 0.78285714 0.78285714 0.80813953] mean value: 0.7886232336773765 MCC on Blind test: 0.01 MCC on Training: 0.61 Running classifier: 23 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=12, random_state=42) Running model pipeline: /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:463: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_CV['source_data'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:490: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_BT['source_data'] = 'BT' Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.01453304 0.01718426 0.01848817 0.01645732 0.0167172 0.0155313 0.01595354 0.01761913 0.01886535 0.01807547] mean value: 0.016942477226257323 key: score_time value: [0.00955224 0.0116663 0.01173735 0.01190019 0.01198578 0.01199055 0.01200271 0.01198006 0.01195931 0.01201296] mean value: 0.011678743362426757 key: test_mcc value: [0.69156407 0.69156407 0.5547002 0.6 0.64705882 0.6 0.50917508 0.82495791 0.51470588 0.76384284] mean value: 0.6397568879103784 key: train_mcc value: [0.80202447 0.70014004 0.58747999 0.55201543 0.91465185 0.68973049 0.66904338 0.82157048 0.85336284 0.8822796 ] mean value: 0.7472298575282299 key: test_fscore value: [0.85 0.85 0.79069767 0.80952381 0.82352941 0.80952381 0.77272727 0.91428571 0.75 0.875 ] mean value: 0.8245287692243917 key: train_fscore value: [0.90207715 0.85393258 0.8042328 0.78961039 0.95765472 0.84916201 0.83977901 0.9020979 0.9209622 0.94155844] mean value: 0.8761067212242309 key: test_precision value: [0.73913043 0.73913043 0.65384615 0.68 0.82352941 0.68 0.62962963 0.88888889 0.75 0.93333333] mean value: 0.7517488287027929 key: train_precision value: [0.82162162 0.74509804 0.67256637 0.65236052 0.9483871 0.73786408 0.72380952 0.96268657 0.97101449 0.92948718] mean value: 0.8164895485198784 key: test_recall value: [1. 1. 1. 1. 0.82352941 1. 1. 0.94117647 0.75 0.82352941] mean value: 0.9338235294117648 key: train_recall value: [1. 1. 1. 1. 0.96710526 1. 1. 0.84868421 0.87581699 0.95394737] mean value: 0.9645553835569316 key: test_accuracy value: [0.82352941 0.82352941 0.73529412 0.76470588 0.82352941 0.76470588 0.70588235 0.91176471 0.75757576 0.87878788] mean value: 0.7989304812834226 key: train_accuracy value: [0.89144737 0.82894737 0.75657895 0.73355263 0.95723684 0.82236842 0.80921053 0.90789474 0.92459016 0.94098361] mean value: 0.8572810612597067 key: test_roc_auc value: [0.82352941 0.82352941 0.73529412 0.76470588 0.82352941 0.76470588 0.70588235 0.91176471 0.75735294 0.88051471] mean value: 0.7990808823529413 key: train_roc_auc value: [0.89144737 0.82894737 0.75657895 0.73355263 0.95723684 0.82236842 0.80921053 0.90789474 0.9247506 0.94102597] mean value: 0.8573013415892673 key: test_jcc value: [0.73913043 0.73913043 0.65384615 0.68 0.7 0.68 0.62962963 0.84210526 0.6 0.77777778] mean value: 0.7041619693976673 key: train_jcc value: [0.82162162 0.74509804 0.67256637 0.65236052 0.91875 0.73786408 0.72380952 0.82165605 0.85350318 0.88957055] mean value: 0.7836799936835639 MCC on Blind test: -0.11 MCC on Training: 0.64 Running classifier: 24 Model_name: XGBoost Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.11489773 0.07142186 0.07784224 0.07480621 0.07327986 0.14564824 0.09994173 0.06823969 0.07569695 0.067307 ] mean value: 0.08690814971923828 key: score_time value: [0.01104379 0.01085663 0.01328564 0.01079965 0.0109005 0.01267266 0.0119257 0.01080823 0.01162219 0.01065612] mean value: 0.011457109451293945 key: test_mcc value: [0.94280904 0.94280904 0.78679579 0.83666003 0.78679579 1. 0.78679579 0.78679579 0.94117647 0.69742172] mean value: 0.8508059472527035 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.97142857 0.97142857 0.89473684 0.91891892 0.89473684 1. 0.89473684 0.89473684 0.96969697 0.85714286] mean value: 0.9267563257036942 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.94444444 0.94444444 0.80952381 0.85 0.80952381 1. 0.80952381 0.80952381 0.94117647 0.83333333] mean value: 0.8751493930905696 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.88235294] mean value: 0.9882352941176471 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.97058824 0.97058824 0.88235294 0.91176471 0.88235294 1. 0.88235294 0.88235294 0.96969697 0.84848485] mean value: 0.9200534759358288 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.97058824 0.97058824 0.88235294 0.91176471 0.88235294 1. 0.88235294 0.88235294 0.97058824 0.84742647] mean value: 0.9200367647058825 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.94444444 0.94444444 0.80952381 0.85 0.80952381 1. 0.80952381 0.80952381 0.94117647 0.75 ] mean value: 0.8668160597572363 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.36 MCC on Training: 0.85 Extracting tts_split_name: 70_30 Total cols in each df: CV df: 8 metaDF: 17 Adding column: Model_name Total cols in bts df: BT_df: 8 First proceeding to rowbind CV and BT dfs: Final output should have: 25 columns Combinig 2 using pd.concat by row ~ rowbind Checking Dims of df to combine: Dim of CV: (24, 8) Dim of BT: (24, 8) 8 Number of Common columns: 8 These are: [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. 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[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... 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Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Œ loky_pàÙ«âVP\W¦¡Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Œ loky_p‘°ÞçU`Œ“þ~Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished ['source_data', 'Precision', 'JCC', 'MCC', 'Recall', 'Accuracy', 'F1', 'ROC_AUC'] Concatenating dfs with different resampling methods [WF]: Split type: 70_30 No. of dfs combining: 2 PASS: 2 dfs successfully combined nrows in combined_df_wf: 48 ncols in combined_df_wf: 8 PASS: proceeding to merge metadata with CV and BT dfs Adding column: Model_name ========================================================= SUCCESS: Ran multiple classifiers ======================================================= ============================================================== Running several classification models (n): 24 List of models: ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('Gaussian NB', GaussianNB()) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('K-Nearest Neighbors', KNeighborsClassifier()) ('LDA', LinearDiscriminantAnalysis()) ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(cv=3, random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Multinomial', MultinomialNB()) ('Naive Bayes', BernoulliNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=12, random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Random Forest', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42)) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=3)) ('SVC', SVC(random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=12, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ================================================================ Running classifier: 1 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.08387327 0.07929468 0.07887197 0.08247805 0.08188486 0.08140182 0.08220649 0.08069587 0.08296013 0.08011198] mean value: 0.08137791156768799 key: score_time value: [0.01455426 0.0145092 0.01562476 0.01598883 0.01504278 0.01767254 0.01476359 0.01468635 0.0150032 0.01476765] mean value: 0.015261316299438476 key: test_mcc value: [ 0.5976143 0.15811388 0.31622777 0.79056942 0. -0.25819889 0.5 0. -0.25819889 0. ] mean value: 0.18461275892402265 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.66666667 0.6 0.72727273 0.90909091 0.5 0.28571429 0.75 0.33333333 0.44444444 0.6 ] mean value: 0.5816522366522366 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.5 0.66666667 0.83333333 0.5 0.33333333 0.75 0.5 0.4 0.5 ] mean value: 0.5983333333333334 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 0.75 0.8 1. 0.5 0.25 0.75 0.25 0.5 0.75] mean value: 0.605 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.77777778 0.55555556 0.66666667 0.88888889 0.5 0.375 0.75 0.5 0.375 0.5 ] mean value: 0.5888888888888889 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.75 0.575 0.65 0.875 0.5 0.375 0.75 0.5 0.375 0.5 ] mean value: 0.585 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.5 0.42857143 0.57142857 0.83333333 0.33333333 0.16666667 0.6 0.2 0.28571429 0.42857143] mean value: 0.43476190476190474 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.03 MCC on Training: 0.18 Running classifier: 2 Model_name: Bagging Classifier Model func: BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3))]) key: fit_time value: [0.09463787 0.11569643 0.13875413 0.12452579 0.13995647 0.15476656 0.14984798 0.12331939 0.1430788 0.14401627] mean value: 0.13285996913909912 key: score_time value: [0.05251861 0.08139348 0.05110526 0.05990767 0.04203057 0.08155751 0.04984403 0.05953622 0.03895807 0.07911968] mean value: 0.059597110748291014 key: test_mcc value: [-0.15811388 0.55 0.5976143 0.35 0. 0. 0.25819889 0.5 0. 0.25819889] mean value: 0.23558982011531 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.28571429 0.75 0.83333333 0.66666667 0.5 0.33333333 0.66666667 0.75 0.33333333 0.66666667] mean value: 0.5785714285714285 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.33333333 0.75 0.71428571 0.75 0.5 0.5 0.6 0.75 0.5 0.6 ] mean value: [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.6s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.6s Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.6s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.6s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.6s Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.6s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.7s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.7s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.7s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.7s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished 0.5997619047619047 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.25 0.75 1. 0.6 0.5 0.25 0.75 0.75 0.25 0.75] mean value: 0.585 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.44444444 0.77777778 0.77777778 0.66666667 0.5 0.5 0.625 0.75 0.5 0.625 ] mean value: 0.6166666666666667 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.425 0.775 0.75 0.675 0.5 0.5 0.625 0.75 0.5 0.625] mean value: 0.6125 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.16666667 0.6 0.71428571 0.5 0.33333333 0.2 0.5 0.6 0.2 0.5 ] mean value: 0.4314285714285715 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.16 MCC on Training: 0.24 Running classifier: 3 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.02350903 0.00999212 0.01000381 0.01006174 0.00960851 0.00998449 0.00991011 0.00998068 0.01039815 0.01023769] mean value: 0.011368632316589355 key: score_time value: [0.00846291 0.00818944 0.00818586 0.00826025 0.00820065 0.0083468 0.00841331 0.00846624 0.00844407 0.00854564] mean value: 0.008351516723632813 key: test_mcc value: [-0.35 0.15811388 0.39528471 0.35 0. 0.25819889 0.57735027 0.77459667 0.25819889 0.57735027] mean value: 0.2999093577644523 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.25 0.6 0.76923077 0.66666667 0.5 0.57142857 0.8 0.85714286 0.57142857 0.8 ] mean value: 0.6385897435897435 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.25 0.5 0.625 0.75 0.5 0.66666667 0.66666667 1. 0.66666667 0.66666667] mean value: 0.6291666666666667 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.25 0.75 1. 0.6 0.5 0.5 1. 0.75 0.5 1. ] mean value: 0.6849999999999999 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.33333333 0.55555556 0.66666667 0.66666667 0.5 0.625 0.75 0.875 0.625 0.75 ] mean value: 0.6347222222222222 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.325 0.575 0.625 0.675 0.5 0.625 0.75 0.875 0.625 0.75 ] mean value: 0.6325000000000001 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.14285714 0.42857143 0.625 0.5 0.33333333 0.4 0.66666667 0.75 0.4 0.66666667] mean value: 0.49130952380952386 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.08 MCC on Training: 0.3 Running classifier: 4 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.00848413 0.0083673 0.00836468 0.00828004 0.0083878 0.0082984 0.00840354 0.00841284 0.00850439 0.00837421] mean value: 0.00838773250579834 key: score_time value: [0.00838041 0.00840449 0.00840163 0.00830317 0.00834846 0.00838995 0.00840044 0.00837779 0.00842285 0.0084331 ] mean value: 0.00838623046875 key: test_mcc value: [-0.15811388 0.1 -0.31622777 0.79056942 -0.25819889 0. 0. -0.57735027 0.25819889 0.37796447] mean value: 0.021684196983643923 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.28571429 0.5 0.25 0.90909091 0.28571429 0.5 0.6 0.4 0.66666667 0.72727273] mean value: 0.5124458874458875 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.33333333 0.5 0.33333333 0.83333333 0.33333333 0.5 0.5 0.33333333 0.6 0.57142857] mean value: 0.4838095238095238 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.25 0.5 0.2 1. 0.25 0.5 0.75 0.5 0.75 1. ] mean value: 0.5700000000000001 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.44444444 0.55555556 0.33333333 0.88888889 0.375 0.5 0.5 0.25 0.625 0.625 ] mean value: 0.5097222222222222 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.425 0.55 0.35 0.875 0.375 0.5 0.5 0.25 0.625 0.625] mean value: 0.5075000000000001 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.16666667 0.33333333 0.14285714 0.83333333 0.16666667 0.33333333 0.42857143 0.25 0.5 0.57142857] mean value: 0.3726190476190476 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.05 MCC on Training: 0.02 Running classifier: 5 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.09250283 0.08907175 0.08715773 0.08948755 0.09298635 0.09297967 0.09313393 0.09295082 0.0929215 0.09287047] mean value: 0.0916062593460083 key: score_time value: [0.01925778 0.01868916 0.01891041 0.01912379 0.01920342 0.01919246 0.01919436 0.01918912 0.01924944 0.01925087] mean value: 0.019126081466674806 key: test_mcc value: [-0.15811388 0.31622777 0.15811388 0.05976143 -0.25819889 0.57735027 0.25819889 0.57735027 0.5 0. ] mean value: 0.20306897348628095 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.28571429 0.57142857 0.5 0.66666667 0.28571429 0.66666667 0.66666667 0.66666667 0.75 0.6 ] mean value: 0.5659523809523809 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.33333333 0.66666667 0.66666667 0.57142857 0.33333333 1. 0.6 1. 0.75 0.5 ] mean value: 0.6421428571428571 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.25 0.5 0.4 0.8 0.25 0.5 0.75 0.5 0.75 0.75] mean value: 0.545 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.44444444 0.66666667 0.55555556 0.55555556 0.375 0.75 0.625 0.75 0.75 0.5 ] mean value: 0.5972222222222222 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.425 0.65 0.575 0.525 0.375 0.75 0.625 0.75 0.75 0.5 ] mean value: 0.5925 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.16666667 0.4 0.33333333 0.5 0.16666667 0.5 0.5 0.5 0.6 0.42857143] mean value: 0.4095238095238095 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.12 MCC on Training: 0.2 Running classifier: 6 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.18573856 0.18248034 0.1897459 0.20039606 0.18959999 0.19254613 0.19143915 0.19292212 0.18818235 0.19062877] mean value: 0.1903679370880127 key: score_time value: [0.00911212 0.00937796 0.00986004 0.00922608 0.01006985 0.0099082 0.0098114 0.00971818 0.0094738 0.00926971] mean value: 0.009582734107971192 key: test_mcc value: [0.39528471 0.31622777 0.39528471 0.31622777 0. 0.25819889 0.25819889 0.5 0.57735027 0.77459667] mean value: 0.3791369665001202 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.4 0.57142857 0.76923077 0.72727273 0.5 0.57142857 0.66666667 0.75 0.66666667 0.88888889] mean value: 0.6511582861582863 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.66666667 0.625 0.66666667 0.5 0.66666667 0.6 0.75 1. 0.8 ] mean value: 0.7274999999999999 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.25 0.5 1. 0.8 0.5 0.5 0.75 0.75 0.5 1. ] mean value: 0.655 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.66666667 0.66666667 0.66666667 0.66666667 0.5 0.625 0.625 0.75 0.75 0.875 ] mean value: 0.6791666666666666 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.625 0.65 0.625 0.65 0.5 0.625 0.625 0.75 0.75 0.875] mean value: 0.6675 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.25 0.4 0.625 0.57142857 0.33333333 0.4 0.5 0.6 0.5 0.8 ] mean value: 0.4979761904761905 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.11 MCC on Training: 0.38 Running classifier: 7 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.00807142 0.00812602 0.00830531 0.00811839 0.00799131 0.00818753 0.00806427 0.00799751 0.00821972 0.00822401] mean value: 0.008130550384521484 key: score_time value: [0.00826812 0.00830245 0.00832725 0.00832415 0.00833321 0.00833654 0.00827837 0.00830412 0.00838923 0.00829482] mean value: 0.008315825462341308 key: test_mcc value: [-0.05976143 0.1 0.79056942 -0.05976143 -0.25819889 0.25819889 0. 0.57735027 0.37796447 0. ] mean value: 0.17263612963075087 key: train_mcc value: [0.46761578 0.60000015 0.46761578 0.55080969 0.63510735 0.52631579 0.55282303 0.55282303 0.65812266 0.5383819 ] mean value: 0.5549615155495695 key: test_fscore value: [0.54545455 0.5 0.90909091 0.28571429 0.28571429 0.57142857 0.5 0.66666667 0.72727273 0.5 ] mean value: 0.5491341991341991 key: train_fscore value: [0.72972973 0.80519481 0.73684211 0.66666667 0.825 0.76315789 0.77333333 0.77922078 0.82666667 0.78571429] mean value: 0.7691526266526266 key: test_precision value: [0.42857143 0.5 0.83333333 0.5 0.33333333 0.66666667 0.5 1. 0.57142857 0.5 ] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) mean value: 0.5833333333333333 key: train_precision value: [0.75 0.79487179 0.71794872 0.95 0.78571429 0.76315789 0.78378378 0.76923077 0.83783784 0.7173913 ] mean value: 0.7869936388471858 key: test_recall value: [0.75 0.5 1. 0.2 0.25 0.5 0.5 0.5 1. 0.5 ] mean value: 0.5700000000000001 key: train_recall value: [0.71052632 0.81578947 0.75675676 0.51351351 0.86842105 0.76315789 0.76315789 0.78947368 0.81578947 0.86842105] mean value: 0.7665007112375534 key: test_accuracy value: [0.44444444 0.55555556 0.88888889 0.44444444 0.375 0.625 0.5 0.75 0.625 0.5 ] mean value: 0.5708333333333333 key: train_accuracy value: [0.73333333 0.8 0.73333333 0.74666667 0.81578947 0.76315789 0.77631579 0.77631579 0.82894737 0.76315789] mean value: 0.773701754385965 key: test_roc_auc value: [0.475 0.55 0.875 0.475 0.375 0.625 0.5 0.75 0.625 0.5 ] mean value: 0.575 key: train_roc_auc value: [0.73364154 0.79978663 0.73364154 0.74359886 0.81578947 0.76315789 0.77631579 0.77631579 0.82894737 0.76315789] mean value: 0.7734352773826458 key: test_jcc value: [0.375 0.33333333 0.83333333 0.16666667 0.16666667 0.4 0.33333333 0.5 0.57142857 0.33333333] mean value: 0.4013095238095238 key: train_jcc value: [0.57446809 0.67391304 0.58333333 0.5 0.70212766 0.61702128 0.63043478 0.63829787 0.70454545 0.64705882] mean value: 0.6271200331112179 MCC on Blind test: 0.08 MCC on Training: 0.17 Running classifier: 8 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.01389742 0.01618981 0.01590919 0.01656747 0.01584458 0.01640177 0.01592779 0.01608777 0.01601267 0.01642156] mean value: 0.015926003456115723 key: score_time value: [0.011307 0.01144385 0.01199937 0.01211023 0.01198006 0.01206207 0.01195908 0.01209235 0.01194596 0.01214051] mean value: 0.011904048919677734 key: test_mcc value: [ 0.1 -0.15811388 0.55 0.31622777 0. 0.57735027 0.57735027 -0.25819889 0.57735027 0. ] mean value: 0.22819658008301355 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.5 0.28571429 0.8 0.72727273 0.5 0.66666667 0.8 0.28571429 0.8 0.66666667] mean value: 0.6032034632034632 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.5 0.33333333 0.8 0.66666667 0.5 1. 0.66666667 0.33333333 0.66666667 0.5 ] mean value: 0.5966666666666667 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 0.25 0.8 0.8 0.5 0.5 1. 0.25 1. 1. ] mean value: 0.6599999999999999 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.55555556 0.44444444 0.77777778 0.66666667 0.5 0.75 0.75 0.375 0.75 0.5 ] mean value: 0.6069444444444445 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.55 0.425 0.775 0.65 0.5 0.75 0.75 0.375 0.75 0.5 ] mean value: 0.6025 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.33333333 0.16666667 0.66666667 0.57142857 0.33333333 0.5 0.66666667 0.16666667 0.66666667 0.5 ] mean value: 0.45714285714285713 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.02 MCC on Training: 0.23 Running classifier: 9 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.02102327 0.00861406 0.00830603 0.00886965 0.00916624 0.00937343 0.00921607 0.00967813 0.00865006 0.00897908] mean value: 0.010187602043151856 key: score_time value: [0.01303816 0.00965405 0.00974798 0.00998712 0.0101397 0.01054668 0.01052475 0.01008892 0.01032281 0.01059246] mean value: 0.01046426296234131 key: test_mcc value: [ 0.1 0.55 -0.1 0.55 -0.25819889 0. 0. -0.25819889 -0.57735027 0. ] mean value: 0.0006251951316052051 key: train_mcc value: [0.39499126 0.38647697 0.3919532 0.33357449 0.39487359 0.50156549 0.40629673 0.55436186 0.44752341 0.43070552] mean value: 0.42423225267563475 key: test_fscore value: [0.5 0.75 0.44444444 0.8 0.44444444 0. 0.5 0.28571429 0. 0.6 ] mean value: 0.43246031746031743 key: train_fscore value: [0.66666667 0.7012987 0.65671642 0.64788732 0.69333333 0.73972603 0.65671642 0.76712329 0.72 0.67647059] mean value: 0.6925938764367046 key: test_precision value: [0.5 0.75 0.5 0.8 0.4 0. 0.5 0.33333333 0. 0.5 ] mean value: 0.42833333333333334 key: train_precision value: [0.74193548 0.69230769 0.73333333 0.67647059 0.7027027 0.77142857 0.75862069 0.8 0.72972973 0.76666667] mean value: 0.7373195457930131 key: test_recall value: [0.5 0.75 0.4 0.8 0.5 0. 0.5 0.25 0. 0.75] mean value: 0.445 key: train_recall value: [0.60526316 0.71052632 0.59459459 0.62162162 0.68421053 0.71052632 0.57894737 0.73684211 0.71052632 0.60526316] mean value: 0.6558321479374112 key: test_accuracy value: [0.55555556 0.77777778 0.44444444 0.77777778 0.375 0.5 0.5 0.375 0.25 0.5 ] mean value: 0.5055555555555555 key: train_accuracy value: [0.69333333 0.69333333 0.69333333 0.66666667 0.69736842 0.75 0.69736842 0.77631579 0.72368421 0.71052632] mean value: 0.7101929824561403 key: test_roc_auc value: [0.55 0.775 0.45 0.775 0.375 0.5 0.5 0.375 0.25 0.5 ] mean value: 0.5050000000000001 key: train_roc_auc value: [0.69452347 0.693101 0.69203414 0.66607397 0.69736842 0.75 0.69736842 0.77631579 0.72368421 0.71052632] mean value: 0.710099573257468 key: test_jcc value: [0.33333333 0.6 0.28571429 0.66666667 0.28571429 0. 0.33333333 0.16666667 0. 0.42857143] mean value: 0.30999999999999994 key: train_jcc value: [0.5 0.54 0.48888889 0.47916667 0.53061224 0.58695652 0.48888889 0.62222222 0.5625 0.51111111] mean value: 0.5310346544414868 MCC on Blind test: 0.1 MCC on Training: 0.0 Running classifier: 10 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.03378892 0.03257155 0.03865385 0.03882504 0.04006076 0.03944945 0.03950715 0.04733706 0.0396421 0.03896618] mean value: 0.038880205154418944 key: score_time value: [0.02852869 0.01162767 0.02283502 0.02251148 0.01720667 0.0233326 0.02168655 0.02184415 0.02238584 0.02202606] mean value: 0.021398472785949706 key: test_mcc value: [ 0.15811388 0.39528471 -0.35 0.35 0. 0.25819889 -0.25819889 0. -0.25819889 0.5 ] mean value: 0.07951997007823053 key: train_mcc value: [0.97368421 0.97368421 0.97366573 1. 1. 0.97402153 0.9486833 1. 1. 1. ] mean value: 0.9843738980161039 key: test_fscore value: [0.6 0.4 0.4 0.66666667 0.33333333 0.57142857 0.44444444 0.5 0.44444444 0.75 ] mean value: 0.511031746031746 key: train_fscore value: [0.98666667 0.98666667 0.98630137 1. 1. 0.98666667 0.97297297 1. 1. 1. ] mean value: 0.9919274342835986 key: test_precision value: [0.5 1. 0.4 0.75 0.5 0.66666667 0.4 0.5 0.4 0.75 ] mean value: 0.5866666666666667 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.75 0.25 0.4 0.6 0.25 0.5 0.5 0.5 0.5 0.75] mean value: 0.5 key: train_recall value: [0.97368421 0.97368421 0.97297297 1. 1. 0.97368421 0.94736842 1. 1. 1. ] mean value: 0.9841394025604553 key: test_accuracy value: [0.55555556 0.66666667 0.33333333 0.66666667 0.5 0.625 0.375 0.5 0.375 0.75 ] mean value: 0.5347222222222222 key: train_accuracy value: [0.98666667 0.98666667 0.98666667 1. 1. 0.98684211 0.97368421 1. 1. 1. ] mean value: 0.9920526315789473 key: test_roc_auc value: [0.575 0.625 0.325 0.675 0.5 0.625 0.375 0.5 0.375 0.75 ] mean value: 0.5325 key: train_roc_auc value: [0.98684211 0.98684211 0.98648649 1. 1. 0.98684211 0.97368421 1. 1. 1. ] mean value: 0.9920697012802275 key: test_jcc value: [0.42857143 0.25 0.25 0.5 0.2 0.4 0.28571429 0.33333333 0.28571429 0.6 ] mean value: 0.3533333333333334 key: train_jcc value: [0.97368421 0.97368421 0.97297297 1. 1. 0.97368421 0.94736842 1. 1. 1. ] mean value: 0.9841394025604553 MCC on Blind test: -0.12 MCC on Training: 0.08 Running classifier: 11 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.02636909 0.04046464 0.03699088 0.04548097 0.02806187 0.02819586 0.03004909 0.02529693 0.02700853 0.0282743 ] mean value: 0.03161921501159668 key: score_time value: [0.01156807 0.01181936 0.01181722 0.01188684 0.01165247 0.01189089 0.01159239 0.01161575 0.01164055 0.01165724] mean value: 0.01171407699584961 key: test_mcc value: [ 0.35 0.55 0.1 0.35 0.25819889 0. 0.5 0.25819889 -0.57735027 0. ] mean value: 0.17890475103046968 key: train_mcc value: [0.8161102 0.83997155 0.83997155 0.81365576 0.8160721 0.8160721 0.84210526 0.92137172 0.84327404 0.79056942] mean value: 0.8339173701413884 key: test_fscore value: [0.66666667 0.75 0.6 0.66666667 0.66666667 0.33333333 0.75 0.57142857 0.4 0.6 ] mean value: 0.6004761904761905 key: train_fscore value: [0.90410959 0.92105263 0.91891892 0.90666667 0.90666667 0.90909091 0.92105263 0.96103896 0.91891892 0.89189189] mean value: 0.9159407785391924 key: test_precision value: [0.6 0.75 0.6 0.75 0.6 0.5 0.75 0.66666667 0.33333333 0.5 ] mean value: 0.605 key: train_precision value: [0.94285714 0.92105263 0.91891892 0.89473684 0.91891892 0.8974359 0.92105263 0.94871795 0.94444444 0.91666667] mean value: 0.9224802043223095 key: test_recall value: [0.75 0.75 0.6 0.6 0.75 0.25 0.75 0.5 0.5 0.75] mean value: 0.62 key: train_recall value: [0.86842105 0.92105263 0.91891892 0.91891892 0.89473684 0.92105263 0.92105263 0.97368421 0.89473684 0.86842105] mean value: 0.9100995732574682 key: test_accuracy value: [0.66666667 0.77777778 0.55555556 0.66666667 0.625 0.5 0.75 0.625 0.25 0.5 ] mean value: 0.5916666666666667 key: train_accuracy value: [0.90666667 0.92 0.92 0.90666667 0.90789474 0.90789474 0.92105263 0.96052632 0.92105263 0.89473684] mean value: 0.9166491228070177 key: test_roc_auc value: [0.675 0.775 0.55 0.675 0.625 0.5 0.75 0.625 0.25 0.5 ] mean value: 0.5925 key: train_roc_auc value: [0.9071835 0.91998578 0.91998578 0.90682788 0.90789474 0.90789474 0.92105263 0.96052632 0.92105263 0.89473684] mean value: 0.9167140825035563 key: test_jcc value: [0.5 0.6 0.42857143 0.5 0.5 0.2 0.6 0.4 0.25 0.42857143] mean value: 0.4407142857142857 key: train_jcc value: [0.825 0.85365854 0.85 0.82926829 0.82926829 0.83333333 0.85365854 0.925 0.85 0.80487805] mean value: 0.8454065040650406 MCC on Blind test: 0.07 MCC on Training: 0.18 Running classifier: 12 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(cv=3, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(cv=3, random_state=42))]) key: fit_time value: [0.25736713 0.27456856 0.24548602 0.24970889 0.25150061 0.27049041 0.26268768 0.24893999 0.29794312 0.25143623] mean value: 0.261012864112854 key: score_time value: [0.01232266 0.01220489 0.0118475 0.01193142 0.01187038 0.01183414 0.01187968 0.01172447 0.01193309 0.0119915 ] mean value: 0.011953973770141601 key: test_mcc value: [-0.05976143 0.15811388 0.1 0.35 0.25819889 0. 0.25819889 0. -0.57735027 0.25819889] mean value: 0.07455988525935568 key: train_mcc value: [0.57594601 1. 0.7341428 0.5737718 0.92137172 1. 1. 1. 1. 1. ] mean value: 0.8805232338463899 key: test_fscore value: [0.54545455 0.6 0.6 0.66666667 0.66666667 0.33333333 0.66666667 0.5 0.4 0.66666667] mean value: 0.5645454545454545 key: train_fscore value: [0.8 1. 0.86111111 0.77777778 0.96 1. 1. 1. 1. 1. ] mean value: 0.9398888888888889 key: test_precision value: [0.42857143 0.5 0.6 0.75 0.6 0.5 0.6 0.5 0.33333333 0.6 ] mean value: 0.5411904761904761 key: train_precision value: [0.76190476 1. 0.88571429 0.8 0.97297297 1. 1. 1. 1. 1. ] mean value: 0.9420592020592021 key: test_recall value: [0.75 0.75 0.6 0.6 0.75 0.25 0.75 0.5 0.5 0.75] mean value: 0.62 key: train_recall value: [0.84210526 1. 0.83783784 0.75675676 0.94736842 1. 1. 1. 1. 1. ] mean value: 0.938406827880512 key: test_accuracy value: [0.44444444 0.55555556 0.55555556 0.66666667 0.625 0.5 0.625 0.5 0.25 0.625 ] mean value: 0.5347222222222222 key: train_accuracy value: [0.78666667 1. 0.86666667 0.78666667 0.96052632 1. 1. 1. 1. 1. ] mean value: 0.9400526315789474 key: test_roc_auc value: [0.475 0.575 0.55 0.675 0.625 0.5 0.625 0.5 0.25 0.625] mean value: 0.54 key: train_roc_auc value: [0.7859175 1. 0.86628734 0.78627312 0.96052632 1. 1. 1. 1. 1. ] mean value: 0.9399004267425319 key: test_jcc value: [0.375 0.42857143 0.42857143 0.5 0.5 0.2 0.5 0.33333333 0.25 0.5 ] mean value: 0.4015476190476191 key: train_jcc value: [0.66666667 1. 0.75609756 0.63636364 0.92307692 1. 1. 1. 1. 1. ] mean value: 0.8982204787082836 MCC on Blind test: -0.04 MCC on Training: 0.07 Running classifier: 13 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [0.53145671 0.4075799 0.37816167 0.42022777 0.39084959 0.61569977 0.3964777 0.40465546 0.38733506 0.37106705] mean value: 0.43035106658935546 key: score_time value: [0.01256824 0.01213598 0.01206136 0.01223803 0.01261783 0.01223683 0.01240253 0.01217365 0.01220632 0.01225471] mean value: 0.01228954792022705 key: test_mcc value: [ 0.8 0.55 0.31622777 0.35 0. 0.37796447 0.57735027 0.25819889 -0.57735027 0. ] mean value: 0.26523911287732266 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.88888889 0.75 0.72727273 0.66666667 0.5 0.4 0.8 0.57142857 0.4 0.6 ] mean value: 0.6304256854256854 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.8 0.75 0.66666667 0.75 0.5 1. 0.66666667 0.66666667 0.33333333 0.5 ] mean value: 0.6633333333333333 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 0.75 0.8 0.6 0.5 0.25 1. 0.5 0.5 0.75] mean value: 0.665 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.88888889 0.77777778 0.66666667 0.66666667 0.5 0.625 0.75 0.625 0.25 0.5 ] mean value: 0.625 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.9 0.775 0.65 0.675 0.5 0.625 0.75 0.625 0.25 0.5 ] mean value: 0.625 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.8 0.6 0.57142857 0.5 0.33333333 0.25 0.66666667 0.4 0.25 0.42857143] mean value: 0.4800000000000001 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.05 MCC on Training: 0.27 Running classifier: 14 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.01178455 0.01175952 0.00983477 0.00866365 0.00946283 0.00948238 0.00918818 0.00935197 0.00979614 0.01295424] mean value: 0.010227823257446289 key: score_time value: [0.01197934 0.01119518 0.00950789 0.00922608 0.00921345 0.00907564 0.00950837 0.00934362 0.00910068 0.00923896] mean value: 0.009738922119140625 key: test_mcc value: [-0.05976143 0.63245553 0.1 0.1 -0.25819889 0.25819889 -0.25819889 0.5 0.5 0.5 ] mean value: 0.2014495211819795 key: train_mcc value: [0.46657183 0.38714641 0.33357041 0.38647697 0.42163702 0.52704628 0.34222378 0.39487359 0.44752341 0.36893239] mean value: 0.4076002102659402 key: test_fscore value: [0.54545455 0.8 0.6 0.6 0.28571429 0.57142857 0.28571429 0.75 0.75 0.75 ] mean value: 0.5938311688311688 key: train_fscore value: [0.73684211 0.70886076 0.66666667 0.68493151 0.71794872 0.75675676 0.67532468 0.7012987 0.72727273 0.69230769] mean value: 0.7068210309182081 key: test_precision value: [0.42857143 0.66666667 0.6 0.6 0.33333333 0.66666667 0.33333333 0.75 0.75 0.75 ] mean value: 0.5878571428571429 key: train_precision value: [0.73684211 0.68292683 0.65789474 0.69444444 0.7 0.77777778 0.66666667 0.69230769 0.71794872 0.675 ] mean value: 0.7001808970518855 key: test_recall value: [0.75 1. 0.6 0.6 0.25 0.5 0.25 0.75 0.75 0.75] mean value: 0.62 key: train_recall value: [0.73684211 0.73684211 0.67567568 0.67567568 0.73684211 0.73684211 0.68421053 0.71052632 0.73684211 0.71052632] mean value: 0.7140825035561876 key: test_accuracy value: [0.44444444 0.77777778 0.55555556 0.55555556 0.375 0.625 0.375 0.75 0.75 0.75 ] mean value: 0.5958333333333334 key: train_accuracy value: [0.73333333 0.69333333 0.66666667 0.69333333 0.71052632 0.76315789 0.67105263 0.69736842 0.72368421 0.68421053] mean value: 0.7036666666666667 key: test_roc_auc value: [0.475 0.8 0.55 0.55 0.375 0.625 0.375 0.75 0.75 0.75 ] mean value: 0.6 key: train_roc_auc value: [0.73328592 0.69274538 0.66678521 0.693101 0.71052632 0.76315789 0.67105263 0.69736842 0.72368421 0.68421053] mean value: 0.7035917496443813 key: test_jcc value: [0.375 0.66666667 0.42857143 0.42857143 0.16666667 0.4 0.16666667 0.6 0.6 0.6 ] mean value: 0.44321428571428567 key: train_jcc value: [0.58333333 0.54901961 0.5 0.52083333 0.56 0.60869565 0.50980392 0.54 0.57142857 0.52941176] mean value: 0.5472526184386798 MCC on Blind test: 0.03 MCC on Training: 0.2 Running classifier: 15 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.01254749 0.00949073 0.00836277 0.00840449 0.00944924 0.00833297 0.00976682 0.00994992 0.01033115 0.00965071] mean value: 0.009628629684448243 key: score_time value: [0.00891542 0.00836563 0.00928378 0.0087502 0.00895047 0.00902796 0.00958538 0.00949192 0.00935817 0.00939989] mean value: 0.009112882614135741 key: test_mcc value: [-0.55 0.35 -0.1 0.35 0. -0.37796447 0. 0. 0.5 0. ] mean value: 0.017203552699077272 key: train_mcc value: [0.53349186 0.63555097 0.57594601 0.5236889 0.55282303 0.76342228 0.45515762 0.61057165 0.60547285 0.63510735] mean value: 0.5891232521505498 key: test_fscore value: [0.22222222 0.66666667 0.44444444 0.66666667 0.33333333 0. 0.5 0.33333333 0.75 0.5 ] mean value: 0.4416666666666666 key: train_fscore value: [0.73529412 0.8 0.77142857 0.68852459 0.77333333 0.88311688 0.69565217 0.78873239 0.80519481 0.825 ] mean value: 0.7766276869163827 key: test_precision value: [0.2 0.6 0.5 0.75 0.5 0. 0.5 0.5 0.75 0.5 ] mean value: 0.48 key: train_precision value: [0.83333333 0.875 0.81818182 0.875 0.78378378 0.87179487 0.77419355 0.84848485 0.79487179 0.78571429] mean value: 0.8260358284551833 key: test_recall value: [0.25 0.75 0.4 0.6 0.25 0. 0.5 0.25 0.75 0.5 ] mean value: 0.425 key: train_recall value: [0.65789474 0.73684211 0.72972973 0.56756757 0.76315789 0.89473684 0.63157895 0.73684211 0.81578947 0.86842105] mean value: 0.7402560455192034 key: test_accuracy value: [0.22222222 0.66666667 0.44444444 0.66666667 0.5 0.375 0.5 0.5 0.75 0.5 ] mean value: 0.5125 key: train_accuracy value: [0.76 0.81333333 0.78666667 0.74666667 0.77631579 0.88157895 0.72368421 0.80263158 0.80263158 0.81578947] mean value: 0.7909298245614035 key: test_roc_auc value: [0.225 0.675 0.45 0.675 0.5 0.375 0.5 0.5 0.75 0.5 ] mean value: 0.515 key: train_roc_auc value: [0.7613798 0.814367 0.7859175 0.7443101 0.77631579 0.88157895 0.72368421 0.80263158 0.80263158 0.81578947] mean value: 0.7908605974395447 key: test_jcc value: [0.125 0.5 0.28571429 0.5 0.2 0. 0.33333333 0.2 0.6 0.33333333] mean value: 0.30773809523809526 key: train_jcc value: [0.58139535 0.66666667 0.62790698 0.525 0.63043478 0.79069767 0.53333333 0.65116279 0.67391304 0.70212766] mean value: 0.6382638276359099 MCC on Blind test: 0.01 MCC on Training: 0.02 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.0131216 0.01373744 0.01427603 0.01366639 0.01331663 0.01367593 0.01348019 0.01419806 0.01426315 0.01340222] mean value: 0.013713765144348144 key: score_time value: [0.00985289 0.01196814 0.01193452 0.01205564 0.0118959 0.01201916 0.01198316 0.01196122 0.01194334 0.01201868] mean value: 0.011763262748718261 key: test_mcc value: [ 0.31622777 0.79056942 0.1 0.35 0. 0. 0.57735027 0.25819889 -0.57735027 0. ] mean value: 0.18149960708060936 key: train_mcc value: [0.52774597 0.76162142 0.89466215 0.8506393 0.8183437 0.74230749 0.63828474 0.89597867 0.8468098 0.89597867] mean value: 0.7872371904300315 key: test_fscore value: [0.66666667 0.85714286 0.6 0.66666667 0.5 0.33333333 0.8 0.57142857 0.4 0.66666667] mean value: 0.6061904761904763 key: train_fscore value: [0.78350515 0.84848485 0.94736842 0.91176471 0.91139241 0.87356322 0.82608696 0.94871795 0.925 0.94871795] mean value: 0.8924601607470741 key: test_precision value: [0.5 1. 0.6 0.75 0.5 0.5 0.66666667 0.66666667 0.33333333 0.5 ] mean value: 0.6016666666666667 key: train_precision value: [0.6440678 1. 0.92307692 1. 0.87804878 0.7755102 0.7037037 0.925 0.88095238 0.925 ] mean value: 0.8655359788912615/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") key: test_recall value: [1. 0.75 0.6 0.6 0.5 0.25 1. 0.5 0.5 1. ] mean value: 0.67 key: train_recall value: [1. 0.73684211 0.97297297 0.83783784 0.94736842 1. 1. 0.97368421 0.97368421 0.97368421] mean value: 0.9416073968705547 key: test_accuracy value: [0.55555556 0.88888889 0.55555556 0.66666667 0.5 0.5 0.75 0.625 0.25 0.5 ] mean value: 0.5791666666666667 key: train_accuracy value: [0.72 0.86666667 0.94666667 0.92 0.90789474 0.85526316 0.78947368 0.94736842 0.92105263 0.94736842] mean value: 0.8821754385964912 key: test_roc_auc value: [0.6 0.875 0.55 0.675 0.5 0.5 0.75 0.625 0.25 0.5 ] mean value: 0.5825 key: train_roc_auc value: [0.71621622 0.86842105 0.9470128 0.91891892 0.90789474 0.85526316 0.78947368 0.94736842 0.92105263 0.94736842] mean value: 0.8818990042674253 key: test_jcc value: [0.5 0.75 0.42857143 0.5 0.33333333 0.2 0.66666667 0.4 0.25 0.5 ] mean value: 0.45285714285714285 key: train_jcc value: [0.6440678 0.73684211 0.9 0.83783784 0.8372093 0.7755102 0.7037037 0.90243902 0.86046512 0.90243902] mean value: 0.8100514114881641 MCC on Blind test: 0.05 MCC on Training: 0.18 Running classifier: 17 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.01615214 0.01476073 0.01452684 0.01520944 0.01426387 0.01451755 0.01457405 0.01462007 0.01477194 0.01466203] mean value: 0.014805865287780762 key: score_time value: [0.0118928 0.01239777 0.0125103 0.01257062 0.01271486 0.01250935 0.01267791 0.0124433 0.01296329 0.01256514] mean value: 0.01252453327178955 key: test_mcc value: [ 0.31622777 0.35 -0.35 0.55 0. 0.25819889 0.25819889 0. 0.25819889 0.25819889] mean value: 0.18990233250054822 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.57142857 0.66666667 0.4 0.8 0.66666667 0.66666667 0.57142857 0.5 0.66666667 0.57142857] mean value: 0.608095238095238 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.66666667 0.6 0.4 0.8 0.5 0.6 0.66666667 0.5 0.6 0.66666667] mean value: 0.6 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 0.75 0.4 0.8 1. 0.75 0.5 0.5 0.75 0.5 ] mean value: 0.645 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.66666667 0.66666667 0.33333333 0.77777778 0.5 0.625 0.625 0.5 0.625 0.625 ] mean value: 0.5944444444444444 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.65 0.675 0.325 0.775 0.5 0.625 0.625 0.5 0.625 0.625] mean value: 0.5925 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.4 0.5 0.25 0.66666667 0.5 0.5 0.4 0.33333333 0.5 0.4 ] mean value: 0.445 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.0 MCC on Training: 0.19 Running classifier: 18 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42))]) key: fit_time value: [0.60816455 0.58708453 0.65986943 0.62378931 0.5719986 0.56754375 0.63128018 0.58022738 0.63399744 0.67184544] mean value: 0.6135800600051879 key: score_time value: [0.13560867 0.16034007 0.15652609 0.16002774 0.20070434 0.16692901 0.19154 0.1202836 0.16529894 0.16659927] mean value: 0.16238577365875245 key: test_mcc value: [0.31622777 0.5976143 0.15811388 0.31622777 0.25819889 0.37796447 0. 0.37796447 0. 0. ] mean value: 0.2402311555474907 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.57142857 0.66666667 0.5 0.72727273 0.66666667 0.4 0.6 0.4 0.5 0.6 ] mean value: 0.5632034632034632 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.66666667 1. 0.66666667 0.66666667 0.6 1. 0.5 1. 0.5 0.5 ] mean value: 0.71 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 0.5 0.4 0.8 0.75 0.25 0.75 0.25 0.5 0.75] mean value: 0.545 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.66666667 0.77777778 0.55555556 0.66666667 0.625 0.625 0.5 0.625 0.5 0.5 ] mean value: 0.6041666666666667 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.65 0.75 0.575 0.65 0.625 0.625 0.5 0.625 0.5 0.5 ] mean value: 0.6 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.4 0.5 0.33333333 0.57142857 0.5 0.25 0.42857143 0.25 0.33333333 0.42857143] mean value: 0.3995238095238095 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.09 MCC on Training: 0.24 Running classifier: 19 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea...age_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42))]) key: fit_time value: [0.88238597 0.9214468 0.98279715 0.88244009 0.88545108 1.00258517 0.89137459 0.90408587 0.91429663 0.94716001] mean value: 0.9214023351669312 key: score_time value: [0.2310257 0.19674897 0.19649458 0.24167323 0.13361382 0.20850635 0.18249989 0.21589422 0.24788213 0.20888734] mean value: 0.20632262229919435 key: test_mcc value: [ 0.1 0.1 0.15811388 0.55 0.25819889 0.37796447 0. 0. -0.25819889 0. ] mean value: 0.12860783560176464 key: train_mcc value: [0.92028493 0.89451381 0.94665718 0.89331437 0.92137172 0.94736842 0.89473684 0.89473684 0.89473684 0.97402153] mean value: 0.9181742489320086 key: test_fscore value: [0.5 0.5 0.5 0.8 0.66666667 0.4 0.6 0.33333333 0.28571429 0.6 ] mean value: 0.5185714285714285 key: train_fscore value: [0.96103896 0.94871795 0.97297297 0.94594595 0.96103896 0.97368421 0.94736842 0.94736842 0.94736842 0.98666667] mean value: 0.9592170930065667 key: test_precision value: [0.5 0.5 0.66666667 0.8 0.6 1. 0.5 0.5 0.33333333 0.5 ] mean value: 0.59 key: train_precision value: [0.94871795 0.925 0.97297297 0.94594595 0.94871795 0.97368421 0.94736842 0.94736842 0.94736842 1. ] mean value: 0.9557144290039027 key: test_recall value: [0.5 0.5 0.4 0.8 0.75 0.25 0.75 0.25 0.25 0.75] mean value: 0.52 key: train_recall value: [0.97368421 0.97368421 0.97297297 0.94594595 0.97368421 0.97368421 0.94736842 0.94736842 0.94736842 0.97368421] mean value: 0.9629445234708391 key: test_accuracy value: [0.55555556 0.55555556 0.55555556 0.77777778 0.625 0.625 0.5 0.5 0.375 0.5 ] mean value: 0.5569444444444445 key: train_accuracy value: [0.96 0.94666667 0.97333333 0.94666667 0.96052632 0.97368421 0.94736842 0.94736842 0.94736842 0.98684211] mean value: 0.9589824561403508 key: test_roc_auc value: [0.55 0.55 0.575 0.775 0.625 0.625 0.5 0.5 0.375 0.5 ] mean value: 0.5575 key: train_roc_auc value: [0.95981508 0.94630156 0.97332859 0.94665718 0.96052632 0.97368421 0.94736842 0.94736842 0.94736842 0.98684211] mean value: 0.9589260312944523 key: test_jcc value: [0.33333333 0.33333333 0.33333333 0.66666667 0.5 0.25 0.42857143 0.2 0.16666667 0.42857143] mean value: 0.364047619047619 key: train_jcc value: [0.925 0.90243902 0.94736842 0.8974359 0.925 0.94871795 0.9 0.9 0.9 0.97368421] mean value: 0.9219645502123036 MCC on Blind test: 0.05 MCC on Training: 0.13 Running classifier: 20 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.03162217 0.03168917 0.04410815 0.03166914 0.03185844 0.02962399 0.04538751 0.03701878 0.03127193 0.04937506] mean value: 0.036362433433532716 key: score_time value: [0.01157665 0.02145457 0.02337527 0.01971889 0.0220964 0.02010298 0.02082086 0.02146029 0.02086854 0.02763414] mean value: 0.02091085910797119 key: test_mcc value: [ 0.63245553 0.31622777 0.1 0.35 0. 0.37796447 0.25819889 0.25819889 -0.25819889 0.25819889] mean value: 0.2293045550554063 key: train_mcc value: [0.92028493 0.97368421 0.94665718 0.97366573 0.97402153 0.97402153 0.94736842 0.97402153 0.97402153 0.97402153] mean value: 0.9631768142789314 key: test_fscore value: [0.8 0.57142857 0.6 0.66666667 0.5 0.4 0.57142857 0.57142857 0.44444444 0.66666667] mean value: 0.5792063492063493 key: train_fscore value: [0.96103896 0.98666667 0.97297297 0.98630137 0.98666667 0.98666667 0.97368421 0.98666667 0.98666667 0.98666667] mean value: 0.9813997514401264 key: test_precision value: [0.66666667 0.66666667 0.6 0.75 0.5 1. 0.66666667 0.66666667 0.4 0.6 ] mean value: 0.6516666666666666 key: train_precision value: [0.94871795 1. 0.97297297 1. 1. 1. 0.97368421 1. 1. 1. ] mean value: 0.9895375132217238 key: test_recall value: [1. 0.5 0.6 0.6 0.5 0.25 0.5 0.5 0.5 0.75] mean value: 0.5700000000000001 key: train_recall value: [0.97368421 0.97368421 0.97297297 0.97297297 0.97368421 0.97368421 0.97368421 0.97368421 0.97368421 0.97368421] mean value: 0.9735419630156471 key: test_accuracy value: [0.77777778 0.66666667 0.55555556 0.66666667 0.5 0.625 0.625 0.625 0.375 0.625 ] mean value: 0.6041666666666667 key: train_accuracy value: [0.96 0.98666667 0.97333333 0.98666667 0.98684211 0.98684211 0.97368421 0.98684211 0.98684211 0.98684211] mean value: 0.981456140350877 key: test_roc_auc value: [0.8 0.65 0.55 0.675 0.5 0.625 0.625 0.625 0.375 0.625] mean value: 0.6050000000000001 key: train_roc_auc value: [0.95981508 0.98684211 0.97332859 0.98648649 0.98684211 0.98684211 0.97368421 0.98684211 0.98684211 0.98684211] mean value: 0.9814366998577524 key: test_jcc value: [0.66666667 0.4 0.42857143 0.5 0.33333333 0.25 0.4 0.4 0.28571429 0.5 ] mean value: 0.4164285714285715 key: train_jcc value: [0.925 0.97368421 0.94736842 0.97297297 0.97368421 0.97368421 0.94871795 0.97368421 0.97368421 0.97368421] mean value: 0.9636164605901447 MCC on Blind test: -0.1 MCC on Training: 0.23 Running classifier: 21 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=3))]) key: fit_time value: [0.06634974 0.05172849 0.07370257 0.03620315 0.08077073 0.08049893 0.07350135 0.08504319 0.07573175 0.0494926 ] mean value: 0.06730225086212158 key: score_time value: [0.01189423 0.0240221 0.01193857 0.01168585 0.01538897 0.02040148 0.01867127 0.02331519 0.02601814 0.01449656] mean value: 0.017783236503601075 key: test_mcc value: [ 0.15811388 0.31622777 0.1 0.35 0.25819889 0.37796447 0.25819889 0.25819889 -0.25819889 0.37796447] mean value: 0.2196668374538034 key: train_mcc value: [0.65362731 0.97368421 0.7341428 0.76031294 1. 0.97402153 1. 0.97402153 0.97402153 1. ] mean value: 0.9043831873097623 key: test_fscore value: [0.6 0.57142857 0.6 0.66666667 0.57142857 0.4 0.57142857 0.57142857 0.44444444 0.72727273] mean value: 0.5724098124098125 key: train_fscore value: [0.82666667 0.98666667 0.86111111 0.88 1. 0.98666667 1. 0.98666667 0.98666667 1. ] mean value: 0.9514444444444443 key: test_precision value: [0.5 0.66666667 0.6 0.75 0.66666667 1. 0.66666667 0.66666667 0.4 0.57142857] mean value: 0.6488095238095238 key: train_precision value: [0.83783784 1. 0.88571429 0.86842105 1. 1. 1. 1. 1. 1. ] mean value: 0.9591973176183703 key: test_recall value: [0.75 0.5 0.6 0.6 0.5 0.25 0.5 0.5 0.5 1. ] mean value: 0.5700000000000001 key: train_recall value: [0.81578947 0.97368421 0.83783784 0.89189189 1. 0.97368421 1. 0.97368421 0.97368421 1. ] mean value: 0.9440256045519204 key: test_accuracy value: [0.55555556 0.66666667 0.55555556 0.66666667 0.625 0.625 0.625 0.625 0.375 0.625 ] mean value: 0.5944444444444444 key: train_accuracy value: [0.82666667 0.98666667 0.86666667 0.88 1. 0.98684211 1. 0.98684211 0.98684211 1. ] mean value: 0.9520526315789473 key: test_roc_auc value: [0.575 0.65 0.55 0.675 0.625 0.625 0.625 0.625 0.375 0.625] mean value: 0.595 key: train_roc_auc value: [0.82681366 0.98684211 0.86628734 0.88015647 1. 0.98684211 1. 0.98684211 0.98684211 1. ] mean value: 0.9520625889046942 key: test_jcc value: [0.42857143 0.4 0.42857143 0.5 0.4 0.25 0.4 0.4 0.28571429 0.57142857] mean value: 0.4064285714285714 key: train_jcc value: [0.70454545 0.97368421 0.75609756 0.78571429 1. 0.97368421 1. 0.97368421 0.97368421 1. ] mean value: 0.9141094143340613 MCC on Blind test: -0.07 MCC on Training: 0.22 Running classifier: 22 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.01888299 0.00976706 0.009444 0.00918603 0.00947762 0.0097158 0.00974393 0.01034665 0.01060438 0.00966835] mean value: 0.010683679580688476 key: score_time value: [0.01032066 0.009408 0.00891948 0.00921202 0.00995421 0.00970817 0.00970626 0.00984979 0.01369786 0.0092628 ] mean value: 0.010003924369812012 key: test_mcc value: [-0.05976143 0.63245553 0.1 0.05976143 -0.25819889 0.25819889 0.25819889 0.25819889 0.57735027 0. ] mean value: 0.18262035807176238 key: train_mcc value: [0.71247852 0.58560367 0.65975905 0.684292 0.71077247 0.74095857 0.64605828 0.76985122 0.76342228 0.71077247] mean value: 0.6983968540616738 key: test_fscore value: [0.54545455 0.8 0.6 0.66666667 0.44444444 0.57142857 0.66666667 0.57142857 0.8 0.6 ] mean value: 0.6266089466089466 key: train_fscore value: [0.86419753 0.80952381 0.83544304 0.84615385 0.85714286 0.875 0.83333333 0.88888889 0.88311688 0.85333333] mean value: 0.8546133520331832 key: test_precision value: [0.42857143 0.66666667 0.6 0.57142857 0.4 0.66666667 0.6 0.66666667 0.66666667 0.5 ] mean value: 0.5766666666666667 key: train_precision value: [0.81395349 0.73913043 0.78571429 0.80487805 0.84615385 0.83333333 0.76086957 0.8372093 0.87179487 0.86486486] mean value: 0.8157902041339364 key: test_recall value: [0.75 1. 0.6 0.8 0.5 0.5 0.75 0.5 1. 0.75] mean value: 0.7150000000000001 key: train_recall value: [0.92105263 0.89473684 0.89189189 0.89189189 0.86842105 0.92105263 0.92105263 0.94736842 0.89473684 0.84210526] mean value: 0.8994310099573258 key: test_accuracy value: [0.44444444 0.77777778 0.55555556 0.55555556 0.375 0.625 0.625 0.625 0.75 0.5 ] mean value: 0.5833333333333334 key: train_accuracy value: [0.85333333 0.78666667 0.82666667 0.84 0.85526316 0.86842105 0.81578947 0.88157895 0.88157895 0.85526316] mean value: 0.8464561403508771 key: test_roc_auc value: [0.475 0.8 0.55 0.525 0.375 0.625 0.625 0.625 0.75 0.5 ] mean value: 0.585 key: train_roc_auc value: [0.85241821 0.78520626 0.82752489 0.84068279 0.85526316 0.86842105 0.81578947 0.88157895 0.88157895 0.85526316] mean value: 0.8463726884779517 key: test_jcc value: [0.375 0.66666667 0.42857143 0.5 0.28571429 0.4 0.5 0.4 0.66666667 0.42857143] mean value: 0.4651190476190477 key: train_jcc value: [0.76086957 0.68 0.7173913 0.73333333 0.75 0.77777778 0.71428571 0.8 0.79069767 0.74418605] mean value: 0.7468541415892276 MCC on Blind test: -0.02 MCC on Training: 0.18 Running classifier: 23 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=12, random_state=42) Running model pipeline: /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:463: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_CV['source_data'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:490: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_BT['source_data'] = 'BT' Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.01012111 0.01195097 0.01279497 0.01297688 0.01242471 0.01312017 0.01252699 0.01278305 0.01319957 0.01307249] mean value: 0.01249709129333496 key: score_time value: [0.00889492 0.01100087 0.01208282 0.0123086 0.01173759 0.01160717 0.01180577 0.01189828 0.01170111 0.01205206] mean value: 0.011508917808532715 key: test_mcc value: [ 0.47809144 0.79056942 0. 0.1 0.57735027 0. 0.25819889 0.57735027 -0.57735027 0.25819889] mean value: 0.24624089074598005 key: train_mcc value: [0.74989398 0.51385791 0.31659469 0.75124363 0.74230749 0.89973541 0.76985122 0.76985122 0.74230749 0.77644535] mean value: 0.7032088393430401 key: test_fscore value: [0.72727273 0.85714286 0.71428571 0.6 0.66666667 0.33333333 0.57142857 0.66666667 0.4 0.66666667] mean value: 0.6203463203463203 key: train_fscore value: [0.88095238 0.59259259 0.7047619 0.87804878 0.83076923 0.95 0.87323944 0.88888889 0.87356322 0.86956522] mean value: 0.834238165085463 key: test_precision value: [0.57142857 1. 0.55555556 0.6 1. 0.5 0.66666667 1. 0.33333333 0.6 ] mean value: 0.6826984126984127 key: train_precision value: [0.80434783 1. 0.54411765 0.8 1. 0.9047619 0.93939394 0.8372093 0.7755102 0.96774194] mean value: 0.8573082759192708 key: test_recall value: [1. 0.75 1. 0.6 0.5 0.25 0.5 0.5 0.5 0.75] mean value: 0.635 key: train_recall value: [0.97368421 0.42105263 1. 0.97297297 0.71052632 1. 0.81578947 0.94736842 1. 0.78947368] mean value: 0.8630867709815078 key: test_accuracy value: [0.66666667 0.88888889 0.55555556 0.55555556 0.75 0.5 0.625 0.75 0.25 0.625 ] mean value: 0.6166666666666666 key: train_accuracy value: [0.86666667 0.70666667 0.58666667 0.86666667 0.85526316 0.94736842 0.88157895 0.88157895 0.85526316 0.88157895] mean value: 0.8329298245614035 key: test_roc_auc value: [0.7 0.875 0.5 0.55 0.75 0.5 0.625 0.75 0.25 0.625] mean value: 0.6125 key: train_roc_auc value: [0.86522048 0.71052632 0.59210526 0.86806543 0.85526316 0.94736842 0.88157895 0.88157895 0.85526316 0.88157895] mean value: 0.833854907539118 key: test_jcc value: [0.57142857 0.75 0.55555556 0.42857143 0.5 0.2 0.4 0.5 0.25 0.5 ] mean value: 0.4655555555555555 key: train_jcc value: [0.78723404 0.42105263 0.54411765 0.7826087 0.71052632 0.9047619 0.775 0.8 0.7755102 0.76923077] mean value: 0.7270042210706916 MCC on Blind test: -0.03 MCC on Training: 0.25 Running classifier: 24 Model_name: XGBoost Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.08744049 0.07790256 0.04532743 0.08711052 0.04177666 0.0424614 0.04644346 0.0907805 0.04183793 0.04227495] mean value: 0.06033558845520019 key: score_time value: [0.01127625 0.01157212 0.01105762 0.0110569 0.0107789 0.01088333 0.0114994 0.01145601 0.01105046 0.01135778] mean value: 0.011198878288269043 key: test_mcc value: [0.31622777 0.1 0.5976143 0.8 0.5 0. 0. 0.25819889 0.25819889 0.25819889] mean value: 0.30884387399255175 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.57142857 0.5 0.83333333 0.88888889 0.75 0.33333333 0.6 0.57142857 0.57142857 0.66666667] mean value: 0.6286507936507937 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.66666667 0.5 0.71428571 1. 0.75 0.5 0.5 0.66666667 0.66666667 0.6 ] mean value: 0.6564285714285714 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 0.5 1. 0.8 0.75 0.25 0.75 0.5 0.5 0.75] mean value: 0.63 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.66666667 0.55555556 0.77777778 0.88888889 0.75 0.5 0.5 0.625 0.625 0.625 ] mean value: 0.6513888888888889 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.65 0.55 0.75 0.9 0.75 0.5 0.5 0.625 0.625 0.625] mean value: 0.6475 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.4 0.33333333 0.71428571 0.8 0.6 0.2 0.42857143 0.4 0.4 0.5 ] mean value: 0.4776190476190476 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.11 MCC on Training: 0.31 Extracting tts_split_name: 70_30 Total cols in each df: CV df: 8 metaDF: 17 Adding column: Model_name Total cols in bts df: BT_df: 8 First proceeding to rowbind CV and BT dfs: Final output should have: 25 columns Combinig 2 using pd.concat by row ~ rowbind Checking Dims of df to combine: Dim of CV: (24, 8) Dim of BT: (24, 8) 8 Number of Common columns: 8 These are: ['source_data', 'Precision', 'JCC', 'MCC', 'Recall', 'Accuracy', 'F1', 'ROC_AUC'] Concatenating dfs with different resampling methods [WF]: Split type: 70_30 No. of dfs combining: 2 PASS: 2 dfs successfully combined nrows in combined_df_wf: 48 ncols in combined_df_wf: 8 [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... 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Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished PASS: proceeding to merge metadata with CV and BT dfs Adding column: Model_name ========================================================= SUCCESS: Ran multiple classifiers ======================================================= ============================================================== Running several classification models (n): 24 List of models: ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('Gaussian NB', GaussianNB()) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('K-Nearest Neighbors', KNeighborsClassifier()) ('LDA', LinearDiscriminantAnalysis()) ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(cv=3, random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Multinomial', MultinomialNB()) ('Naive Bayes', BernoulliNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=12, random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Random Forest', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42)) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=3)) ('SVC', SVC(random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=12, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ================================================================ Running classifier: 1 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.12953472 0.13300872 0.13528442 0.13114142 0.12742591 0.12672067 0.1265974 0.12901402 0.13006425 0.12893939] mean value: 0.12977309226989747 key: score_time value: [0.0147984 0.01605535 0.016155 0.01476264 0.0148437 0.0160048 0.01483154 0.01497054 0.01474428 0.01484466] mean value: 0.015201091766357422 key: test_mcc value: [0.94280904 1. 0.88852332 0.94280904 0.83666003 0.83666003 0.88852332 0.94280904 0.7333588 0.75735294] mean value: 0.8769505549890658 key: train_mcc value: [1. 1. 1. 0.99344255 1. 1. 1. 1. 0.99346377 1. ] mean value: 0.9986906324377449 key: test_fscore value: [0.97142857 1. 0.94444444 0.97142857 0.91891892 0.91891892 0.94444444 0.97142857 0.86486486 0.88235294] mean value: 0.9388230247053777 key: train_fscore value: [1. 1. 1. 0.99672131 1. 1. 1. 1. 0.99674267 1. ] mean value: 0.9993463982485181 key: test_precision value: [0.94444444 1. 0.89473684 0.94444444 0.85 0.85 0.89473684 0.94444444 0.76190476 0.88235294] mean value: 0.8967064720625093 key: train_precision value: [1. 1. 1. 0.99346405 1. 1. 1. 1. 0.99350649 1. ] mean value: 0.9986970545794076 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.88235294] mean value: 0.9882352941176471 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.97058824 1. 0.94117647 0.97058824 0.91176471 0.91176471 0.94117647 0.97058824 0.84848485 0.87878788] mean value: 0.9344919786096255 key: train_accuracy value: [1. 1. 1. 0.99671053 1. 1. 1. 1. 0.99672131 1. ] mean value: 0.9993431837791199 key: test_roc_auc value: [0.97058824 1. 0.94117647 0.97058824 0.91176471 0.91176471 0.94117647 0.97058824 0.85294118 0.87867647] mean value: 0.9349264705882353 key: train_roc_auc value: [1. 1. 1. 0.99671053 1. 1. 1. 1. 0.99671053 1. ] mean value: 0.9993421052631579 key: test_jcc value: [0.94444444 1. 0.89473684 0.94444444 0.85 0.85 0.89473684 0.94444444 0.76190476 0.78947368] mean value: 0.8874185463659148 key: train_jcc value: [1. 1. 1. 0.99346405 1. 1. 1. 1. 0.99350649 1. ] mean value: 0.9986970545794076 MCC on Blind test: 0.03 MCC on Training: 0.88 Running classifier: 2 Model_name: Bagging Classifier Model func: BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3))]) key: fit_time value: [0.15101528 0.19959998 0.19836736 0.19486833 0.20117474 0.19059992 0.15921497 0.19627309 0.20915771 0.19933438] mean value: 0.18996057510375977 key: score_time value: [0.04145885 0.04133224 0.08104634 0.04641509 0.03857946 0.0421927 0.04524994 0.06002092 0.07043433 0.03790975] mean value: 0.05046396255493164 key: test_mcc value: [0.94280904 0.88852332 0.88852332 0.94280904 0.73854895 0.83666003 0.88852332 0.94280904 0.68599434 0.75735294] mean value: 0.8512553328818683 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.6s remaining: 1.2s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.6s remaining: 1.3s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.7s remaining: 1.4s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.7s remaining: 1.4s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.7s remaining: 0.2s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.7s remaining: 1.4s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.7s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.7s remaining: 0.2s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.7s remaining: 1.4s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.7s remaining: 1.5s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.7s remaining: 1.5s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.7s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.8s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.8s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.8s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.8s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.8s remaining: 0.3s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.8s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.8s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.8s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.8s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.8s remaining: 1.6s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.8s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.8s remaining: 0.3s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.8s remaining: 1.6s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.8s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.8s remaining: 0.3s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.8s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.8s remaining: 0.3s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.8s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [0.97142857 0.94444444 0.94444444 0.97142857 0.87179487 0.91891892 0.94444444 0.97142857 0.84210526 0.88235294] mean value: 0.9262791042667204 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.94444444 0.89473684 0.89473684 0.94444444 0.77272727 0.85 0.89473684 0.94444444 0.72727273 0.88235294] mean value: 0.8749896800825592 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.88235294] mean value: 0.9882352941176471 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.97058824 0.94117647 0.94117647 0.97058824 0.85294118 0.91176471 0.94117647 0.97058824 0.81818182 0.87878788] mean value: 0.9196969696969697 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.97058824 0.94117647 0.94117647 0.97058824 0.85294118 0.91176471 0.94117647 0.97058824 0.82352941 0.87867647] mean value: 0.9202205882352942 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.94444444 0.89473684 0.89473684 0.94444444 0.77272727 0.85 0.89473684 0.94444444 0.72727273 0.78947368] mean value: 0.8657017543859649 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.06 MCC on Training: 0.85 Running classifier: 3 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.01986909 0.01779628 0.01603842 0.01674747 0.01654387 0.01659894 0.017313 0.01750159 0.01734066 0.01655555] mean value: 0.01723048686981201 key: score_time value: [0.0090518 0.00960755 0.00952268 0.00918722 0.0095048 0.00888848 0.00940681 0.00868177 0.00889277 0.00884795] mean value: 0.009159183502197266 key: test_mcc value: [0.83666003 1. 0.78679579 0.94280904 0.73854895 0.78679579 0.83666003 0.88852332 0.78215389 0.81985294] mean value: 0.8418799772155248 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.91891892 1. 0.89473684 0.97142857 0.87179487 0.89473684 0.91891892 0.94444444 0.88888889 0.90909091] mean value: 0.9212959207696049 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.85 1. 0.80952381 0.94444444 0.77272727 0.80952381 0.85 0.89473684 0.8 0.9375 ] mean value: 0.8668456178324601 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.88235294] mean value: 0.9882352941176471 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.91176471 1. 0.88235294 0.97058824 0.85294118 0.88235294 0.91176471 0.94117647 0.87878788 0.90909091] mean value: 0.9140819964349376 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.91176471 1. 0.88235294 0.97058824 0.85294118 0.88235294 0.91176471 0.94117647 0.88235294 0.90992647] mean value: 0.9145220588235295 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.85 1. 0.80952381 0.94444444 0.77272727 0.80952381 0.85 0.89473684 0.8 0.83333333] mean value: 0.8564289511657932 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.17 MCC on Training: 0.84 Running classifier: 4 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.01152897 0.01038122 0.01036191 0.01048851 0.01057076 0.01021552 0.01068282 0.01065493 0.01036072 0.01054287] mean value: 0.01057882308959961 key: score_time value: [0.00970674 0.00967288 0.00959253 0.00954914 0.00944757 0.00948787 0.00956893 0.00948477 0.00974941 0.00952339] mean value: 0.009578323364257813 key: test_mcc value: [0.69156407 0.88852332 0.88852332 0.94280904 0.69156407 0.73854895 0.88852332 0.78679579 0.78215389 0.75735294] mean value: 0.8056358709511182 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.85 0.94444444 0.94444444 0.97142857 0.85 0.87179487 0.94444444 0.89473684 0.88888889 0.88235294] mean value: 0.9042535448727399 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.73913043 0.89473684 0.89473684 0.94444444 0.73913043 0.77272727 0.89473684 0.80952381 0.8 0.88235294] mean value: 0.8371519863753003 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.88235294] mean value: 0.9882352941176471 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.82352941 0.94117647 0.94117647 0.97058824 0.82352941 0.85294118 0.94117647 0.88235294 0.87878788 0.87878788] mean value: 0.8934046345811051 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.82352941 0.94117647 0.94117647 0.97058824 0.82352941 0.85294118 0.94117647 0.88235294 0.88235294 0.87867647] mean value: 0.89375 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.73913043 0.89473684 0.89473684 0.94444444 0.73913043 0.77272727 0.89473684 0.80952381 0.8 0.78947368] mean value: 0.8278640606787059 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.06 MCC on Training: 0.81 Running classifier: 5 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.10777116 0.10193563 0.10909343 0.10666609 0.1081953 0.10812259 0.10234332 0.10909438 0.10563564 0.09941292] mean value: 0.10582704544067383 key: score_time value: [0.01844597 0.01990509 0.01861429 0.01865697 0.01727605 0.0182035 0.01814032 0.01806092 0.01738143 0.01725888] mean value: 0.0181943416595459 key: test_mcc value: [1. 0.94280904 0.94280904 0.94280904 0.88852332 0.83666003 0.94280904 0.94280904 0.88561489 0.81985294] mean value: 0.9144696377799597 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 0.97142857 0.97142857 0.97142857 0.94444444 0.91891892 0.97142857 0.97142857 0.94117647 0.90909091] mean value: 0.9570773600185364 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.94444444 0.94444444 0.94444444 0.89473684 0.85 0.94444444 0.94444444 0.88888889 0.9375 ] mean value: 0.9293347953216374 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.88235294] mean value: 0.9882352941176471 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 0.97058824 0.97058824 0.97058824 0.94117647 0.91176471 0.97058824 0.97058824 0.93939394 0.90909091] mean value: 0.9554367201426024 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 0.97058824 0.97058824 0.97058824 0.94117647 0.91176471 0.97058824 0.97058824 0.94117647 0.90992647] mean value: 0.9556985294117647 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 0.94444444 0.94444444 0.94444444 0.89473684 0.85 0.94444444 0.94444444 0.88888889 0.83333333] mean value: 0.9189181286549708 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.02 MCC on Training: 0.91 Running classifier: 6 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.42609167 0.42978787 0.42365408 0.46676922 0.44814706 0.44860625 0.44983888 0.45270395 0.44556522 0.44198942] mean value: 0.44331536293029783 key: score_time value: [0.00893974 0.00907993 0.00922751 0.01052737 0.00930548 0.01011229 0.01012158 0.00920081 0.00929451 0.00977755] mean value: 0.009558677673339844 key: test_mcc value: [0.88852332 1. 0.83666003 1. 0.83666003 0.88852332 0.88852332 0.94280904 0.78215389 0.88561489] mean value: 0.8949467818981267 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.94444444 1. 0.91891892 1. 0.91891892 0.94444444 0.94444444 0.97142857 0.88888889 0.9375 ] mean value: 0.9468988631488632 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.89473684 1. 0.85 1. 0.85 0.89473684 0.89473684 0.94444444 0.8 1. ] mean value: 0.9128654970760234 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.88235294] mean value: 0.9882352941176471 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.94117647 1. 0.91176471 1. 0.91176471 0.94117647 0.94117647 0.97058824 0.87878788 0.93939394] mean value: 0.9435828877005348 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.94117647 1. 0.91176471 1. 0.91176471 0.94117647 0.94117647 0.97058824 0.88235294 0.94117647] mean value: 0.9441176470588235 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.89473684 1. 0.85 1. 0.85 0.89473684 0.89473684 0.94444444 0.8 0.88235294] mean value: 0.9011007911936705 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.18 MCC on Training: 0.89 Running classifier: 7 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.00974584 0.00903797 0.00929832 0.0090847 0.01160884 0.00941753 0.01024246 0.01015043 0.00987744 0.01034403] mean value: 0.009880757331848145 key: score_time value: [0.00921845 0.00865173 0.00862455 0.0086937 0.00947022 0.0094192 0.00897622 0.00902867 0.00861263 0.0095768 ] mean value: 0.009027218818664551 key: test_mcc value: [0.5976143 0.66575029 0.35355339 0.53311399 0. 0.47809144 0.11952286 0.41464421 0.39338235 0.21437323] mean value: 0.37700460746472103 key: train_mcc value: [0.51476459 0.49916874 0.55282303 0.51861886 0.46542718 0.48035673 0.54754344 0.46116549 0.54755762 0.47701789] mean value: 0.5064443559575884 key: test_fscore value: [0.81081081 0.84210526 0.68571429 0.75 0.56410256 0.75675676 0.59459459 0.6875 0.6875 0.66666667] mean value: 0.7045750941803574 key: train_fscore value: [0.76582278 0.76452599 0.77922078 0.77300613 0.74846626 0.74267101 0.78787879 0.73717949 0.77377049 0.74683544] mean value: 0.7619377170224251 key: test_precision value: [0.75 0.76190476 0.66666667 0.8 0.5 0.7 0.55 0.73333333 0.6875 0.59090909] mean value: 0.6740313852813853 key: train_precision value: [0.73780488 0.71428571 0.76923077 0.72413793 0.70114943 0.73548387 0.73033708 0.71875 0.77631579 0.7195122 ] mean value: 0.7327007652102167 key: test_recall value: [0.88235294 0.94117647 0.70588235 0.70588235 0.64705882 0.82352941 0.64705882 0.64705882 0.6875 0.76470588] mean value: 0.7452205882352941 key: train_recall value: [0.79605263 0.82236842 0.78947368 0.82894737 0.80263158 0.75 0.85526316 0.75657895 0.77124183 0.77631579] mean value: 0.7948873409012728 key: test_accuracy value: [0.79411765 0.82352941 0.67647059 0.76470588 0.5 0.73529412 0.55882353 0.70588235 0.6969697 0.60606061] mean value: 0.6861853832442069 key: train_accuracy value: [0.75657895 0.74671053 0.77631579 0.75657895 0.73026316 0.74013158 0.76973684 0.73026316 0.77377049 0.73770492] mean value: 0.7518054357204487 key: test_roc_auc value: [0.79411765 0.82352941 0.67647059 0.76470588 0.5 0.73529412 0.55882353 0.70588235 0.69669118 0.60110294] mean value: 0.6856617647058822 key: train_roc_auc value: [0.75657895 0.74671053 0.77631579 0.75657895 0.73026316 0.74013158 0.76973684 0.73026316 0.77377881 0.7378311 ] mean value: 0.7518188854489164 key: test_jcc value: [0.68181818 0.72727273 0.52173913 0.6 0.39285714 0.60869565 0.42307692 0.52380952 0.52380952 0.5 ] mean value: 0.5503078805252718 key: train_jcc value: [0.62051282 0.61881188 0.63829787 0.63 0.59803922 0.59067358 0.65 0.58375635 0.63101604 0.5959596 ] mean value: 0.6157067348775183 MCC on Blind test: -0.06 MCC on Training: 0.38 Running classifier: 8 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.07332897 0.06669378 0.04770112 0.04669547 0.0637691 0.047328 0.07643414 0.05328965 0.04776239 0.09925699] mean value: 0.062225961685180665 key: score_time value: [0.02061605 0.01325011 0.01333404 0.02123213 0.01342964 0.01327157 0.04564476 0.01325583 0.01324558 0.02148581] mean value: 0.01887655258178711 key: test_mcc value: [0.83666003 0.94280904 0.83666003 0.88852332 0.78679579 0.78679579 0.78679579 0.88852332 0.78215389 0.67967383] mean value: 0.8215390829031015 key: train_mcc value: [0.96127552 0.96127552 0.96127552 0.96762892 0.96762892 0.96127552 0.95496057 0.96127552 0.95509799 0.96140461] mean value: 0.9613098638183069 key: test_fscore value: [0.91891892 0.97142857 0.91891892 0.94444444 0.89473684 0.89473684 0.89473684 0.94444444 0.88888889 0.85 ] mean value: 0.9121254713359976 key: train_fscore value: [0.98064516 0.98064516 0.98064516 0.98381877 0.98381877 0.98064516 0.97749196 0.98064516 0.97763578 0.98064516] mean value: 0.9806636252357406 key: test_precision value: [0.85 0.94444444 0.85 0.89473684 0.80952381 0.80952381 0.80952381 0.89473684 0.8 0.73913043] mean value: 0.8401619992009008 key: train_precision value: [0.96202532 0.96202532 0.96202532 0.96815287 0.96815287 0.96202532 0.95597484 0.96202532 0.95625 0.96202532] mean value: 0.962068247398555 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.91176471 0.97058824 0.91176471 0.94117647 0.88235294 0.88235294 0.88235294 0.94117647 0.87878788 0.81818182] mean value: 0.9020499108734402 key: train_accuracy value: [0.98026316 0.98026316 0.98026316 0.98355263 0.98355263 0.98026316 0.97697368 0.98026316 0.97704918 0.98032787] mean value: 0.9802771786022433 key: test_roc_auc value: [0.91176471 0.97058824 0.91176471 0.94117647 0.88235294 0.88235294 0.88235294 0.94117647 0.88235294 0.8125 ] mean value: 0.9018382352941176 key: train_roc_auc value: [0.98026316 0.98026316 0.98026316 0.98355263 0.98355263 0.98026316 0.97697368 0.98026316 0.97697368 0.98039216] mean value: 0.9802760577915375 key: test_jcc value: [0.85 0.94444444 0.85 0.89473684 0.80952381 0.80952381 0.80952381 0.89473684 0.8 0.73913043] mean value: 0.8401619992009008 key: train_jcc value: [0.96202532 0.96202532 0.96202532 0.96815287 0.96815287 0.96202532 0.95597484 0.96202532 0.95625 0.96202532] mean value: 0.962068247398555 MCC on Blind test: -0.08 MCC on Training: 0.82 Running classifier: 9 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.01828957 0.01034021 0.01047683 0.01012349 0.01018405 0.01013494 0.00874543 0.00976396 0.00973845 0.00978971] mean value: 0.010758662223815918 key: score_time value: [0.0272584 0.01264071 0.01741385 0.01721597 0.01730132 0.01546526 0.01358056 0.01230168 0.01744771 0.01274133] mean value: 0.016336679458618164 key: test_mcc value: [0.56582515 0.70710678 0.56582515 0.71713717 0.42365927 0.47809144 0.47140452 0.52941176 0.52029875 0.45876334] mean value: 0.5437523337039392 key: train_mcc value: [0.72023901 0.73690714 0.73690714 0.76504898 0.76949779 0.7228974 0.70986468 0.70766865 0.73527645 0.71245581] mean value: 0.7316763029137446 key: test_fscore value: [0.8 0.85714286 0.8 0.86486486 0.73684211 0.75675676 0.74285714 0.76470588 0.76470588 0.75675676] mean value: 0.7844632248347418 key: train_fscore value: [0.86549708 0.87315634 0.87315634 0.88622754 0.88757396 0.86746988 0.86135693 0.86053412 0.87315634 0.86227545] mean value: 0.8710403997381626 key: test_precision value: [0.69565217 0.83333333 0.69565217 0.8 0.66666667 0.7 0.72222222 0.76470588 0.72222222 0.7 ] mean value: 0.7300454674623473 key: train_precision value: [0.77894737 0.79144385 0.79144385 0.81318681 0.80645161 0.8 0.78074866 0.78378378 0.79569892 0.79120879] mean value: 0.7932913657871212 key: test_recall value: [0.94117647 0.88235294 0.94117647 0.94117647 0.82352941 0.82352941 0.76470588 0.76470588 0.8125 0.82352941] mean value: 0.8518382352941177 key: train_recall /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( value: [0.97368421 0.97368421 0.97368421 0.97368421 0.98684211 0.94736842 0.96052632 0.95394737 0.96732026 0.94736842] mean value: 0.9658109735122119 key: test_accuracy value: [0.76470588 0.85294118 0.76470588 0.85294118 0.70588235 0.73529412 0.73529412 0.76470588 0.75757576 0.72727273] mean value: 0.766131907308378 key: train_accuracy value: [0.84868421 0.85855263 0.85855263 0.875 0.875 0.85526316 0.84539474 0.84539474 0.85901639 0.84918033] mean value: 0.8570038826574633 key: test_roc_auc value: [0.76470588 0.85294118 0.76470588 0.85294118 0.70588235 0.73529412 0.73529412 0.76470588 0.75919118 0.72426471] mean value: 0.7659926470588234 key: train_roc_auc value: [0.84868421 0.85855263 0.85855263 0.875 0.875 0.85526316 0.84539474 0.84539474 0.85866013 0.8495012 ] mean value: 0.857000343997248 key: test_jcc value: [0.66666667 0.75 0.66666667 0.76190476 0.58333333 0.60869565 0.59090909 0.61904762 0.61904762 0.60869565] mean value: 0.6474967061923583 key: train_jcc value: [0.7628866 0.77486911 0.77486911 0.79569892 0.79787234 0.76595745 0.75647668 0.75520833 0.77486911 0.75789474] mean value: 0.7716602393859564 MCC on Blind test: -0.04 MCC on Training: 0.54 Running classifier: 10 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.02684832 0.03294778 0.04926205 0.06502223 0.06627131 0.05781913 0.07366276 0.09227991 0.04939389 0.07005525] mean value: 0.05835626125335693 key: score_time value: [0.01210093 0.01210618 0.0220232 0.01789618 0.0244751 0.01931119 0.01835465 0.02256608 0.02403355 0.02125096] mean value: 0.019411802291870117 key: test_mcc value: [0.56582515 0.56582515 0.69156407 0.77005354 0.69156407 0.66575029 0.83666003 0.78679579 0.83276554 0.52710164] mean value: 0.6933905280108532 key: train_mcc value: [0.9544639 0.94810737 0.9486833 0.94129888 0.96127552 0.96060947 0.94244297 0.93623886 0.94883835 0.9364543 ] mean value: 0.9478412941014301 key: test_fscore value: [0.8 0.8 0.85 0.88888889 0.85 0.84210526 0.91891892 0.89473684 0.91428571 0.78947368] mean value: 0.8548409311567206 key: train_fscore value: [0.97734628 0.97419355 0.97435897 0.97087379 0.98064516 0.98039216 0.97124601 0.96815287 0.97452229 0.96815287] mean value: 0.9739883937491541 key: test_precision value: [0.69565217 0.69565217 0.73913043 0.84210526 0.73913043 0.76190476 0.85 0.80952381 0.84210526 0.71428571] mean value: 0.768949002942138 key: train_precision value: [0.96178344 0.9556962 0.95 0.95541401 0.96202532 0.97402597 0.94409938 0.9382716 0.95031056 0.9382716 ] mean value: 0.9529898093007357 key: test_recall value: [0.94117647 0.94117647 1. 0.94117647 1. 0.94117647 1. 1. 1. 0.88235294] mean value: 0.9647058823529413 key: train_recall value: [0.99342105 0.99342105 1. 0.98684211 1. 0.98684211 1. 1. 1. 1. ] mean value: 0.9960526315789474 key: test_accuracy value: [0.76470588 0.76470588 0.82352941 0.88235294 0.82352941 0.82352941 0.91176471 0.88235294 0.90909091 0.75757576] mean value: 0.834313725490196 key: train_accuracy value: [0.97697368 0.97368421 0.97368421 0.97039474 0.98026316 0.98026316 0.97039474 0.96710526 0.97377049 0.96721311] mean value: 0.9733746764452114 key: test_roc_auc value: [0.76470588 0.76470588 0.82352941 0.88235294 0.82352941 0.82352941 0.91176471 0.88235294 0.91176471 0.75367647] mean value: 0.8341911764705883 key: train_roc_auc value: [0.97697368 0.97368421 0.97368421 0.97039474 0.98026316 0.98026316 0.97039474 0.96710526 0.97368421 0.96732026] mean value: 0.9733767629858961 key: test_jcc value: [0.66666667 0.66666667 0.73913043 0.8 0.73913043 0.72727273 0.85 0.80952381 0.84210526 0.65217391] mean value: 0.7492669915896462 key: train_jcc value: [0.9556962 0.94968553 0.95 0.94339623 0.96202532 0.96153846 0.94409938 0.9382716 0.95031056 0.9382716 ] mean value: 0.9493294889296836 MCC on Blind test: -0.03 MCC on Training: 0.69 Running classifier: 11 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.04367542 0.03580499 0.03674459 0.03795791 0.03777218 0.04822969 0.0608952 0.06002045 0.05610037 0.06658554] mean value: 0.04837863445281983 key: score_time value: [0.01243329 0.01256847 0.01203132 0.01210642 0.01271462 0.01264095 0.01269245 0.01256728 0.01262593 0.01285839] mean value: 0.012523913383483886 key: test_mcc value: [0.70710678 0.76470588 0.54470478 0.70710678 0.47140452 0.73854895 0.64705882 0.78679579 0.81919377 0.45387763] mean value: 0.6640503704757729 key: train_mcc value: [0.75006493 0.73709738 0.81133788 0.78372626 0.80936818 0.79606986 0.77739326 0.750585 0.77087921 0.79814911] mean value: 0.7784671065587249 key: test_fscore value: [0.85714286 0.88235294 0.78947368 0.84848485 0.74285714 0.87179487 0.82352941 0.89473684 0.90322581 0.74285714] mean value: 0.8356455548845443 key: train_fscore value: [0.87581699 0.87012987 0.90793651 0.89389068 0.90553746 0.89836066 0.89102564 0.87741935 0.88745981 0.90095847] mean value: 0.8908535431184662 key: test_precision value: [0.83333333 0.88235294 0.71428571 0.875 0.72222222 0.77272727 0.82352941 0.80952381 0.93333333 0.72222222] mean value: 0.8088530260589083 key: train_precision value: [0.87012987 0.85897436 0.87730061 0.87421384 0.89677419 0.89542484 0.86875 0.86075949 0.87341772 0.8757764 ] mean value: 0.8751521321934245 key: test_recall value: [0.88235294 0.88235294 0.88235294 0.82352941 0.76470588 1. 0.82352941 1. 0.875 0.76470588] mean value: 0.8698529411764706 key: train_recall value: [0.88157895 0.88157895 0.94078947 0.91447368 0.91447368 0.90131579 0.91447368 0.89473684 0.90196078 0.92763158] mean value: 0.9073013415892672 key: test_accuracy value: [0.85294118 0.88235294 0.76470588 0.85294118 0.73529412 0.85294118 0.82352941 0.88235294 0.90909091 0.72727273] mean value: 0.8283422459893048 key: train_accuracy value: [0.875 0.86842105 0.90460526 0.89144737 0.90460526 0.89802632 0.88815789 0.875 0.8852459 0.89836066] mean value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( 0.8888869715271787 key: test_roc_auc value: [0.85294118 0.88235294 0.76470588 0.85294118 0.73529412 0.85294118 0.82352941 0.88235294 0.90808824 0.72610294] mean value: 0.828125 key: train_roc_auc value: [0.875 0.86842105 0.90460526 0.89144737 0.90460526 0.89802632 0.88815789 0.875 0.88519092 0.89845631] mean value: 0.8888910388716891 key: test_jcc value: [0.75 0.78947368 0.65217391 0.73684211 0.59090909 0.77272727 0.7 0.80952381 0.82352941 0.59090909] mean value: 0.7216088378351133 key: train_jcc value: [0.77906977 0.77011494 0.83139535 0.80813953 0.82738095 0.81547619 0.80346821 0.7816092 0.79768786 0.81976744] mean value: 0.8034109443175594 MCC on Blind test: 0.01 MCC on Training: 0.66 Running classifier: 12 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(cv=3, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(cv=3, random_state=42))]) key: fit_time value: [0.47356844 0.46855187 0.46244097 0.60374284 0.47560763 0.56968856 0.47423339 0.53245258 0.54214501 0.47961783] mean value: 0.5082049131393432 key: score_time value: [0.01199102 0.01204085 0.01218247 0.01195765 0.01197004 0.01214194 0.01206398 0.01218271 0.01210332 0.01202703] mean value: 0.012066102027893067 key: test_mcc value: [0.83666003 0.73854895 0.78679579 0.83666003 0.69156407 0.78679579 0.83666003 0.83666003 0.94117647 0.75735294] mean value: 0.8048874123524016 key: train_mcc value: [1. 0.98045414 0.96127552 1. 1. 0.98045414 1. 1. 1. 1. ] mean value: 0.9922183805908471 key: test_fscore value: [0.91891892 0.87179487 0.89473684 0.91891892 0.85 0.89473684 0.91891892 0.91891892 0.96969697 0.88235294] mean value: 0.9038994142554515 key: train_fscore value: [1. 0.99022801 0.98064516 1. 1. 0.99022801 1. 1. 1. 1. ] mean value: 0.9961101187348955 key: test_precision value: [0.85 0.77272727 0.80952381 0.85 0.73913043 0.80952381 0.85 0.85 0.94117647 0.88235294] mean value: 0.8354434738322206 key: train_precision value: [1. 0.98064516 0.96202532 1. 1. 0.98064516 1. 1. 1. 1. ] mean value: 0.9923315639036341 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.88235294] mean value: 0.9882352941176471 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.91176471 0.85294118 0.88235294 0.91176471 0.82352941 0.88235294 0.91176471 0.91176471 0.96969697 0.87878788] mean value: 0.8936720142602497 key: train_accuracy value: [1. 0.99013158 0.98026316 1. 1. 0.99013158 1. 1. 1. 1. ] mean value: 0.9960526315789474 key: test_roc_auc value: [0.91176471 0.85294118 0.88235294 0.91176471 0.82352941 0.88235294 0.91176471 0.91176471 0.97058824 0.87867647] mean value: 0.89375 key: train_roc_auc value: [1. 0.99013158 0.98026316 1. 1. 0.99013158 1. 1. 1. 1. ] mean value: 0.9960526315789473 key: test_jcc value: [0.85 0.77272727 0.80952381 0.85 0.73913043 0.80952381 0.85 0.85 0.94117647 0.78947368] mean value: 0.8261555481356261 key: train_jcc value: [1. 0.98064516 0.96202532 1. 1. 0.98064516 1. 1. 1. 1. ] mean value: 0.9923315639036341 MCC on Blind test: 0.01 MCC on Training: 0.8 Running classifier: 13 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [1.36812282 1.51557684 1.33718395 1.47176242 1.48009396 1.5972836 1.48570395 1.75319791 1.49704432 1.55559206] mean value: 1.5061561822891236 key: score_time value: [0.01245689 0.01332545 0.01305413 0.01292038 0.01263571 0.02727675 0.01472354 0.01459908 0.0169251 0.01274657] mean value: 0.01506636142730713 key: test_mcc value: [0.94280904 0.88852332 0.78679579 0.94280904 0.73854895 0.78679579 0.88852332 0.88852332 0.94117647 0.63944497] mean value: 0.8443950006109467 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.97142857 0.94444444 0.89473684 0.97142857 0.87179487 0.89473684 0.94444444 0.94444444 0.96969697 0.83333333] mean value: 0.9240489335226177 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.94444444 0.89473684 0.80952381 0.94444444 0.77272727 0.80952381 0.89473684 0.89473684 0.94117647 0.78947368] mean value: 0.8695524461778332 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.88235294] mean value: 0.9882352941176471 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.97058824 0.94117647 0.88235294 0.97058824 0.85294118 0.88235294 0.94117647 0.94117647 0.96969697 0.81818182] mean value: 0.9170231729055258 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.97058824 0.94117647 0.88235294 0.97058824 0.85294118 0.88235294 0.94117647 0.94117647 0.97058824 0.81617647] mean value: 0.9169117647058824 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.94444444 0.89473684 0.80952381 0.94444444 0.77272727 0.80952381 0.89473684 0.89473684 0.94117647 0.71428571] mean value: 0.862033649185352 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.07 MCC on Training: 0.84 Running classifier: 14 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.01267505 0.01249003 0.00947571 0.00901103 0.00878525 0.00887847 0.00894332 0.00931549 0.00926661 0.00888777] mean value: 0.009772872924804688 key: score_time value: [0.01140738 0.01047063 0.00872564 0.00834227 0.00843596 0.00836825 0.00865173 0.00844359 0.00845051 0.00840402] mean value: 0.008969998359680176 key: test_mcc value: [0.47140452 0.53311399 0.35355339 0.53311399 0.11785113 0.61545745 0.18156826 0.47140452 0.35892848 0.08648817] mean value: 0.3722883911770262 key: train_mcc value: [0.39694209 0.45395719 0.4234049 0.45474521 0.39528471 0.45419317 0.45395719 0.40846086 0.37859391 0.46283432] mean value: 0.42823735605339763 key: test_fscore value: [0.74285714 0.77777778 0.66666667 0.75 0.57142857 0.82051282 0.53333333 0.74285714 0.56 0.59459459] mean value: 0.6760028050028051 key: train_fscore value: [0.68055556 0.72607261 0.69444444 0.71864407 0.70512821 0.72240803 0.72607261 0.69594595 0.67576792 0.72297297] mean value: 0.7068012351209776 key: test_precision value: [0.72222222 0.73684211 0.6875 0.8 0.55555556 0.72727273 0.61538462 0.72222222 0.77777778 0.55 ] mean value: 0.6894777225698279 key: train_precision value: [0.72058824 0.72847682 0.73529412 0.74125874 0.6875 0.73469388 0.72847682 0.71527778 0.70714286 0.74305556] mean value: 0.7241764804611235 key: test_recall value: [0.76470588 0.82352941 0.64705882 0.70588235 0.58823529 0.94117647 0.47058824 0.76470588 0.4375 0.64705882] mean value: 0.6790441176470589 key: train_recall value: [0.64473684 0.72368421 0.65789474 0.69736842 0.72368421 0.71052632 0.72368421 0.67763158 0.64705882 0.70394737] mean value: 0.6910216718266253 key: test_accuracy value: [0.73529412 0.76470588 0.67647059 0.76470588 0.55882353 0.79411765 0.58823529 0.73529412 0.66666667 0.54545455] mean value: 0.6829768270944742 key: train_accuracy value: [0.69736842 0.72697368 0.71052632 0.72697368 0.69736842 0.72697368 0.72697368 0.70394737 0.68852459 0.73114754] mean value: 0.7136777394305437 key: test_roc_auc value: [0.73529412 0.76470588 0.67647059 0.76470588 0.55882353 0.79411765 0.58823529 0.73529412 0.65992647 0.54227941] mean value: 0.681985294117647 key: train_roc_auc value: [0.69736842 0.72697368 0.71052632 0.72697368 0.69736842 0.72697368 0.72697368 0.70394737 0.68866099 0.73105865] mean value: 0.7136824905400758 key: test_jcc value: [0.59090909 0.63636364 0.5 0.6 0.4 0.69565217 0.36363636 0.59090909 0.38888889 0.42307692] mean value: 0.5189436167697037 key: train_jcc value: [0.51578947 0.56994819 0.53191489 0.56084656 0.54455446 0.56544503 0.56994819 0.53367876 0.51030928 0.56613757] mean value: 0.5468572383793109 MCC on Blind test: -0.11 MCC on Training: 0.37 Running classifier: 15 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.01902032 0.0104394 0.01069427 0.01077604 0.01073456 0.01081395 0.0107522 0.01056838 0.01086545 0.01080298] mean value: 0.011546754837036132 key: score_time value: [0.00976348 0.00959969 0.00904441 0.00963736 0.00954771 0.00956368 0.00963211 0.00952387 0.0096662 0.00969911] mean value: 0.009567761421203613 key: test_mcc value: [0.53311399 0.64705882 0.64705882 0.35856858 0.23570226 0.58925565 0.36927447 0.65158377 0.58285506 0.33210558] mean value: 0.4946577013198472 key: train_mcc value: [0.63883569 0.58510845 0.62664308 0.61570504 0.61847459 0.61057165 0.57791046 0.60088591 0.63948738 0.58831631] mean value: 0.6101938573880407 key: test_fscore value: [0.75 0.82352941 0.82352941 0.64516129 0.62857143 0.8 0.62068966 0.83333333 0.75862069 0.68571429] mean value: 0.7369149506298627 key: train_fscore value: [0.81481481 0.77304965 0.80546075 0.79442509 0.81045752 0.78873239 0.77031802 0.79037801 0.81848185 0.78498294] mean value: 0.7951101020284876 key: test_precision value: [0.8 0.82352941 0.82352941 0.71428571 0.61111111 0.77777778 0.75 0.78947368 0.84615385 0.66666667] mean value: 0.7602527623735054 key: train_precision value: [0.83448276 0.83846154 0.83687943 0.84444444 0.80519481 0.84848485 0.83206107 0.82733813 0.82666667 0.81560284] mean value: 0.8309616529575233 key: test_recall value: [0.70588235 0.82352941 0.82352941 0.58823529 0.64705882 0.82352941 0.52941176 0.88235294 0.6875 0.70588235] mean value: 0.7216911764705882 key: train_recall value: [0.79605263 0.71710526 0.77631579 0.75 0.81578947 0.73684211 0.71710526 0.75657895 0.81045752 0.75657895] mean value: 0.7632825937392501 key: test_accuracy value: [0.76470588 0.82352941 0.82352941 0.67647059 0.61764706 0.79411765 0.67647059 0.82352941 0.78787879 0.66666667] mean value: 0.7454545454545455 key: train_accuracy value: [0.81907895 0.78947368 0.8125 0.80592105 0.80921053 0.80263158 0.78618421 0.79934211 0.81967213 0.79344262] mean value: 0.8037456859361519 key: test_roc_auc value: [0.76470588 0.82352941 0.82352941 0.67647059 0.61764706 0.79411765 0.67647059 0.82352941 0.78492647 0.66544118] mean value: 0.7450367647058824 key: train_roc_auc value: [0.81907895 0.78947368 0.8125 0.80592105 0.80921053 0.80263158 0.78618421 0.79934211 0.81970244 0.79332215] mean value: 0.8037366701066391 key: test_jcc value: [0.6 0.7 0.7 0.47619048 0.45833333 0.66666667 0.45 0.71428571 0.61111111 0.52173913] mean value: 0.5898326432022084 key: train_jcc value: [0.6875 0.6300578 0.67428571 0.65895954 0.68131868 0.65116279 0.62643678 0.65340909 0.69273743 0.64606742] mean value: 0.6601935245758753 MCC on Blind test: -0.16 MCC on Training: 0.49 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=12, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.01855326 0.0223217 0.01734066 0.02235055 0.02024794 0.01735473 0.01641893 0.018641 0.02016282 0.0226984 ] mean value: 0.01960899829864502 key: score_time value: [0.00867391 0.01197386 0.0118084 0.01190162 0.01178813 0.01182389 0.01176882 0.01186442 0.01186228 0.01189089] mean value: 0.01153562068939209 key: test_mcc value: [0.4152274 0.70710678 0.51639778 0.76470588 0.42365927 0.21821789 0.46291005 0.77005354 0.83276554 0.52710164] mean value: 0.5638145776614545 key: train_mcc value: [0.53131486 0.83641374 0.50520718 0.88226658 0.79189281 0.61348198 0.58747999 0.87547379 0.78054158 0.83060556] mean value: 0.7234678065359529 key: test_fscore value: [0.45454545 0.85714286 0.66666667 0.88235294 0.66666667 0.41666667 0.75555556 0.88888889 0.91428571 0.78947368] mean value: 0.7292245095805467 key: train_fscore value: [0.62162162 0.91961415 0.6 0.93959732 0.87591241 0.71428571 0.8042328 0.93645485 0.89212828 0.91566265] mean value: 0.8219509792229592 key: test_precision value: [1. 0.83333333 0.9 0.88235294 0.76923077 0.71428571 0.60714286 0.84210526 0.84210526 0.71428571] mean value: 0.8104841855770648 key: train_precision value: [0.98571429 0.89937107 0.97058824 0.95890411 0.98360656 0.98837209 0.67256637 0.95238095 0.80526316 0.84444444] mean value: 0.9061211276581689 key: test_recall value: [0.29411765 0.88235294 0.52941176 0.88235294 0.58823529 0.29411765 1. 0.94117647 1. 0.88235294] mean value: 0.7294117647058822 key: train_recall value: [0.45394737 0.94078947 0.43421053 0.92105263 0.78947368 0.55921053 1. 0.92105263 1. 1. ] mean value: 0.8019736842105264 key: test_accuracy value: [0.64705882 0.85294118 0.73529412 0.88235294 0.70588235 0.58823529 0.67647059 0.88235294 0.90909091 0.75757576] mean value: 0.7637254901960785 key: train_accuracy value: [0.72368421 0.91776316 0.71052632 0.94078947 0.88815789 0.77631579 0.75657895 0.9375 0.87868852 0.90819672] mean value: 0.8438201035375323 key: test_roc_auc value: [0.64705882 0.85294118 0.73529412 0.88235294 0.70588235 0.58823529 0.67647059 0.88235294 0.91176471 0.75367647] mean value: 0.7636029411764707 key: train_roc_auc value: [0.72368421 0.91776316 0.71052632 0.94078947 0.88815789 0.77631579 0.75657895 0.9375 0.87828947 0.90849673] mean value: 0.8438101995184037 key: test_jcc value: [0.29411765 0.75 0.5 0.78947368 0.5 0.26315789 0.60714286 0.8 0.84210526 0.65217391] mean value: 0.5998171259350422 key: train_jcc value: [0.45098039 0.85119048 0.42857143 0.88607595 0.77922078 0.55555556 0.67256637 0.88050314 0.80526316 0.84444444] mean value: 0.7154371699736876 MCC on Blind test: 0.02 MCC on Training: 0.56 Running classifier: 17 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.03575063 0.02446628 0.0250628 0.02367306 0.02374768 0.02393913 0.02483582 0.02531624 0.02541947 0.02485919] mean value: 0.025707030296325685 key: score_time value: [0.01239848 0.01242065 0.01246595 0.01252604 0.01256895 0.01257229 0.01242805 0.01232886 0.0125246 0.0124054 ] mean value: 0.012463927268981934 key: test_mcc value: [1. 1. 1. 1. 1. 0.94280904 1. 1. 1. 0.88561489] mean value: 0.9828423927122157 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 1. 1. 1. 1. 0.97142857 1. 1. 1. 0.9375 ] mean value: 0.9908928571428571 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 1. 1. 1. 0.94444444 1. 1. 1. 1. ] mean value: 0.9944444444444445 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.88235294] mean value: 0.9882352941176471 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 1. 1. 1. 1. 0.97058824 1. 1. 1. 0.93939394] mean value: 0.9909982174688057 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 1. 1. 1. 1. 0.97058824 1. 1. 1. 0.94117647] mean value: 0.9911764705882353 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 1. 1. 1. 1. 0.94444444 1. 1. 1. 0.88235294] mean value: 0.9826797385620916 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.0 MCC on Training: 0.98 Running classifier: 18 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42))]) key: fit_time value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( [0.65001106 0.70930314 0.59736252 0.6500299 0.58273363 0.66176343 0.61056685 0.62859511 0.61048794 0.67317653] mean value: 0.6374030113220215 key: score_time value: [0.17155385 0.19915152 0.21433568 0.16930175 0.17940068 0.16715336 0.14616036 0.17001843 0.16418266 0.16815591] mean value: 0.17494142055511475 key: test_mcc value: [0.88852332 0.88852332 0.88852332 0.94280904 0.83666003 0.83666003 0.94280904 0.88852332 0.88561489 0.81985294] mean value: 0.8818499229503398 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.94444444 0.94444444 0.94444444 0.97142857 0.91891892 0.91891892 0.97142857 0.94444444 0.94117647 0.90909091] mean value: 0.9408740138151902 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.89473684 0.89473684 0.89473684 0.94444444 0.85 0.85 0.94444444 0.89473684 0.88888889 0.9375 ] mean value: 0.8994225146198831 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.88235294] mean value: 0.9882352941176471 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.94117647 0.94117647 0.94117647 0.97058824 0.91176471 0.91176471 0.97058824 0.94117647 0.93939394 0.90909091] mean value: 0.937789661319073 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.94117647 0.94117647 0.94117647 0.97058824 0.91176471 0.91176471 0.97058824 0.94117647 0.94117647 0.90992647] mean value: 0.9380514705882353 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.89473684 0.89473684 0.89473684 0.94444444 0.85 0.85 0.94444444 0.89473684 0.88888889 0.83333333] mean value: 0.8890058479532165 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.06 MCC on Training: 0.88 Running classifier: 19 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea...age_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42))]) key: fit_time value: [1.01170444 0.95893955 0.92228556 0.98800874 0.93302107 0.91484046 0.93267918 0.95594954 0.89792824 0.99043345] mean value: 0.950579023361206 key: score_time value: [0.24221873 0.21520686 0.2238996 0.19837141 0.27058506 0.19068885 0.32996678 0.22300315 0.2516017 0.20852971] mean value: 0.2354071855545044 key: test_mcc value: [0.82495791 0.82495791 0.88852332 0.82495791 0.5976143 0.83666003 0.83666003 0.88852332 0.83276554 0.63944497] mean value: 0.7995065241235412 key: train_mcc value: [0.96729368 0.96729368 0.97402153 0.96060947 0.96085908 0.96762892 0.96762892 0.97402153 0.98051616 0.96773647] mean value: 0.9687609470785198 key: test_fscore value: [0.91428571 0.91428571 0.94444444 0.91428571 0.81081081 0.91891892 0.91891892 0.94444444 0.91428571 0.83333333] mean value: 0.9028013728013728 key: train_fscore value: [0.98371336 0.98371336 0.98701299 0.98039216 0.98051948 0.98381877 0.98381877 0.98701299 0.99029126 0.98381877] mean value: 0.9844111894321455 key: test_precision value: [0.88888889 0.88888889 0.89473684 0.88888889 0.75 0.85 0.85 0.89473684 0.84210526 0.78947368] mean value: 0.8537719298245614 key: train_precision value: [0.97419355 0.97419355 0.97435897 0.97402597 0.96794872 0.96815287 0.96815287 0.97435897 0.98076923 0.96815287] mean value: 0.9724307566962178 key: test_recall value: [0.94117647 0.94117647 1. 0.94117647 0.88235294 1. 1. 1. 1. 0.88235294] mean value: 0.9588235294117646 key: train_recall value: [0.99342105 0.99342105 1. 0.98684211 0.99342105 1. 1. 1. 1. 1. ] mean value: 0.9967105263157894 key: test_accuracy value: [0.91176471 0.91176471 0.94117647 0.91176471 0.79411765 0.91176471 0.91176471 0.94117647 0.90909091 0.81818182] mean value: 0.8962566844919786 key: train_accuracy value: [0.98355263 0.98355263 0.98684211 0.98026316 0.98026316 0.98355263 0.98355263 0.98684211 0.99016393 0.98360656] mean value: 0.9842191544434857 key: test_roc_auc value: [0.91176471 0.91176471 0.94117647 0.91176471 0.79411765 0.91176471 0.91176471 0.94117647 0.91176471 0.81617647] mean value: 0.8963235294117646 key: train_roc_auc value: [0.98355263 0.98355263 0.98684211 0.98026316 0.98026316 0.98355263 0.98355263 0.98684211 0.99013158 0.98366013] mean value: 0.9842212762297902 key: test_jcc value: [0.84210526 0.84210526 0.89473684 0.84210526 0.68181818 0.85 0.85 0.89473684 0.84210526 0.71428571] mean value: 0.8253998632946 key: train_jcc value: [0.96794872 0.96794872 0.97435897 0.96153846 0.96178344 0.96815287 0.96815287 0.97435897 0.98076923 0.96815287] mean value: 0.9693165115139637 MCC on Blind test: 0.15 MCC on Training: 0.8 Running classifier: 20 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.03295875 0.03509355 0.03656864 0.03661466 0.03526044 0.0350647 0.0352037 0.03532934 0.03500772 0.03525209] mean value: 0.035235357284545896 key: score_time value: [0.023139 0.02331972 0.02283573 0.0129168 0.02136946 0.02178645 0.02168512 0.02176881 0.02246165 0.02236772] mean value: 0.021365046501159668 key: test_mcc value: [0.70710678 0.71713717 0.71713717 0.88852332 0.5976143 0.73854895 0.76470588 0.73854895 0.81985294 0.51676076] mean value: 0.7205936210928878 key: train_mcc value: [0.87653787 0.90368528 0.90289991 0.93623886 0.90463044 0.93623886 0.90368528 0.91783672 0.89698972 0.90946338] mean value: 0.9088206322171526 key: test_fscore value: [0.85714286 0.86486486 0.86486486 0.94444444 0.81081081 0.87179487 0.88235294 0.87179487 0.90909091 0.77777778] mean value: 0.8654939213762743 key: train_fscore value: [0.93929712 0.95238095 0.95207668 0.96815287 0.95268139 0.96815287 0.95238095 0.95899054 0.94936709 0.95512821] mean value: 0.9548608657188934 key: test_precision value: [0.83333333 0.8 0.8 0.89473684 0.75 0.77272727 0.88235294 0.77272727 0.88235294 0.73684211] mean value: 0.812507270850924 key: train_precision value: [0.91304348 0.9202454 0.92546584 0.9382716 0.91515152 0.9382716 0.9202454 0.92121212 0.9202454 0.93125 ] mean value: 0.9243402359329386 key: test_recall value: [0.88235294 0.94117647 0.94117647 1. 0.88235294 1. 0.88235294 1. 0.9375 0.82352941] mean value: 0.9290441176470589 key: train_recall value: [0.96710526 0.98684211 0.98026316 1. 0.99342105 1. 0.98684211 1. 0.98039216 0.98026316] mean value: 0.9875128998968007 key: test_accuracy value: [0.85294118 0.85294118 0.85294118 0.94117647 0.79411765 0.85294118 0.88235294 0.85294118 0.90909091 0.75757576] mean value: 0.8549019607843137 key: train_accuracy value: [0.9375 0.95065789 0.95065789 0.96710526 0.95065789 0.96710526 0.95065789 0.95723684 0.94754098 0.95409836] mean value: 0.9533218291630717 key: test_roc_auc value: [0.85294118 0.85294118 0.85294118 0.94117647 0.79411765 0.85294118 0.88235294 0.85294118 0.90992647 0.75551471] mean value: 0.854779411764706 key: train_roc_auc value: [0.9375 0.95065789 0.95065789 0.96710526 0.95065789 0.96710526 0.95065789 0.95723684 0.94743292 0.95418387] mean value: 0.9533195734434126 key: test_jcc value: [0.75 0.76190476 0.76190476 0.89473684 0.68181818 0.77272727 0.78947368 0.77272727 0.83333333 0.63636364] mean value: 0.765498974709501 key: train_jcc value: [0.88554217 0.90909091 0.90853659 0.9382716 0.90963855 0.9382716 0.90909091 0.92121212 0.90361446 0.91411043] mean value: 0.913737934480708 MCC on Blind test: -0.09 MCC on Training: 0.72 Running classifier: 21 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=3))]) key: fit_time value: [0.10883284 0.11249566 0.1132803 0.11389232 0.12924552 0.11378431 0.11409283 0.1140914 0.11297917 0.1183815 ] mean value: 0.11510758399963379 key: score_time value: [0.02345705 0.02521992 0.02198386 0.01685166 0.02096415 0.02293754 0.02219939 0.02095437 0.02166891 0.02037692] mean value: 0.021661376953125 key: test_mcc value: [0.70710678 0.83666003 0.56582515 0.71713717 0.69156407 0.61545745 0.83666003 0.78679579 0.81985294 0.63944497] mean value: 0.7216504386948083 key: train_mcc value: [0.87653787 0.9486833 0.94129888 0.94769663 0.97402153 0.94769663 0.95496057 0.94244297 0.89698972 0.94263679] mean value: 0.9372964893636514 key: test_fscore value: [0.85714286 0.91891892 0.8 0.86486486 0.85 0.82051282 0.91891892 0.89473684 0.90909091 0.83333333] mean value: 0.8667519464887885 key: train_fscore value: [0.93929712 0.97435897 0.97087379 0.97402597 0.98701299 0.97402597 0.97749196 0.97124601 0.94936709 0.97124601] mean value: 0.9688945883234255 key: test_precision value: [0.83333333 0.85 0.69565217 0.8 0.73913043 0.72727273 0.85 0.80952381 0.88235294 0.78947368] mean value: 0.7976739104212519 key: train_precision value: [0.91304348 0.95 0.95541401 0.96153846 0.97435897 0.96153846 0.95597484 0.94409938 0.9202454 0.94409938] mean value: 0.9480312387739896 key: test_recall value: [0.88235294 1. 0.94117647 0.94117647 1. 0.94117647 1. 1. 0.9375 0.88235294] mean value: 0.9525735294117649 key: train_recall value: [0.96710526 1. 0.98684211 0.98684211 1. 0.98684211 1. 1. 0.98039216 1. ] mean value: 0.9908023735810113 key: test_accuracy value: [0.85294118 0.91176471 0.76470588 0.85294118 0.82352941 0.79411765 0.91176471 0.88235294 0.90909091 0.81818182] mean value: 0.8521390374331551 key: train_accuracy value: [0.9375 0.97368421 0.97039474 0.97368421 0.98684211 0.97368421 0.97697368 0.97039474 0.94754098 0.9704918 ] mean value: 0.9681190681622089 key: test_roc_auc value: [0.85294118 0.91176471 0.76470588 0.85294118 0.82352941 0.79411765 0.91176471 0.88235294 0.90992647 0.81617647] mean value: 0.8520220588235293 key: train_roc_auc value: [0.9375 0.97368421 0.97039474 0.97368421 0.98684211 0.97368421 0.97697368 0.97039474 0.94743292 0.97058824] mean value: 0.9681179050567597 key: test_jcc value: [0.75 0.85 0.66666667 0.76190476 0.73913043 0.69565217 0.85 0.80952381 0.83333333 0.71428571] mean value: 0.7670496894409937 key: train_jcc value: [0.88554217 0.95 0.94339623 0.94936709 0.97435897 0.94936709 0.95597484 0.94409938 0.90361446 0.94409938] mean value: 0.9399819605026554 MCC on Blind test: -0.05 MCC on Training: 0.72 Running classifier: 22 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.03079915 0.01393628 0.01446581 0.01362014 0.01388741 0.01386523 0.0136776 0.01638436 0.01560545 0.01533341] mean value: 0.01615748405456543 key: score_time value: [0.01143956 0.01005316 0.01003957 0.01002288 0.01039958 0.01015878 0.00997567 0.01116371 0.01005554 0.01053071] mean value: 0.010383915901184083 key: test_mcc value: [0.78679579 0.83666003 0.53311399 0.76470588 0.58925565 0.73854895 0.23904572 0.82495791 0.76212918 0.39338235] mean value: 0.6468595455695586 key: train_mcc value: [0.77176042 0.76554733 0.76481485 0.7613557 0.80831934 0.77800131 0.70469504 0.75738252 0.75136678 0.7908009 ] mean value: 0.765404419558212 key: test_fscore value: [0.89473684 0.91891892 0.77777778 0.88235294 0.8 0.87179487 0.58064516 0.91428571 0.86666667 0.70588235] mean value: 0.8213061246957182 key: train_fscore value: [0.88888889 0.88607595 0.88535032 0.8847352 0.9068323 0.89171975 0.85530547 0.88102894 0.87820513 0.89677419] mean value: 0.8854916129477312 key: test_precision value: [0.80952381 0.85 0.73684211 0.88235294 0.77777778 0.77272727 0.64285714 0.88888889 0.92857143 0.70588235] mean value: 0.7995423719727126 key: train_precision value: [0.85889571 0.85365854 0.85802469 0.84023669 0.85882353 0.86419753 0.83647799 0.86163522 0.86163522 0.87974684] mean value: 0.8573331943247352 key: test_recall value: [1. 1. 0.82352941 0.88235294 0.82352941 1. 0.52941176 0.94117647 0.8125 0.70588235] mean value: 0.8518382352941176 key: train_recall value: [0.92105263 0.92105263 0.91447368 0.93421053 0.96052632 0.92105263 0.875 0.90131579 0.89542484 0.91447368] mean value: 0.9158582731338148 key: test_accuracy value: [0.88235294 0.91176471 0.76470588 0.88235294 0.79411765 0.85294118 0.61764706 0.91176471 0.87878788 0.6969697 ] mean value: 0.8193404634581105 key: train_accuracy value: [0.88486842 0.88157895 0.88157895 0.87828947 0.90131579 0.88815789 0.85197368 0.87828947 0.87540984 0.89508197] mean value: 0.8816544434857635 key: test_roc_auc value: [0.88235294 0.91176471 0.76470588 0.88235294 0.79411765 0.85294118 0.61764706 0.91176471 0.87683824 0.69669118] mean value: 0.8191176470588235 key: train_roc_auc value: [0.88486842 0.88157895 0.88157895 0.87828947 0.90131579 0.88815789 0.85197368 0.87828947 0.875344 0.89514534] mean value: 0.8816541967664259 key: test_jcc value: [0.80952381 0.85 0.63636364 0.78947368 0.66666667 0.77272727 0.40909091 0.84210526 0.76470588 0.54545455] mean value: 0.7086111669548203 key: train_jcc value: [0.8 0.79545455 0.79428571 0.79329609 0.82954545 0.8045977 0.74719101 0.78735632 0.78285714 0.8128655 ] mean value: 0.7947449477828817 MCC on Blind test: 0.01 MCC on Training: 0.65 Running classifier: 23 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.0157001 0.01610589 0.01657009 0.01672649 0.01935983 0.01637387 0.01672864 0.01703477 0.01688671 0.03319001] mean value: 0.018467640876770018 key: score_time value: [0.0097611 0.01122546 0.01186109 0.01192737 0.0119276 0.01189303 0.01191545 0.01199126 0.0119977 0.01239514] mean value: 0.011689519882202149 key: test_mcc value: [0.36514837 0.69156407 0.61545745 0.64549722 0.47809144 0.73854895 0.54470478 0.66575029 0.7333588 0.45387763] mean value: 0.5931999007152029 key: train_mcc value: [0.36650833 0.70536299 0.83248604 0.64057345 0.92861683 0.81873663 0.76880521 0.69565894 0.64400917 0.86322394] mean value: 0.7263981525763817 key: test_fscore value: [0.72340426 0.85 0.82051282 0.74074074 0.75675676 0.87179487 0.78947368 0.8 0.86486486 0.74285714] mean value: 0.7960405137056873 key: train_fscore value: [0.72380952 0.85633803 0.91034483 0.74796748 0.96463023 0.91017964 0.88821752 0.8030888 0.82926829 0.93247588] mean value: 0.8566320227713202 key: test_precision value: [0.56666667 0.73913043 0.72727273 1. 0.7 0.77272727 0.71428571 0.92307692 0.76190476 0.72222222] mean value: 0.7627286722938896 key: train_precision value: [0.56716418 0.74876847 0.95652174 0.9787234 0.94339623 0.83516484 0.82122905 0.97196262 0.70833333 0.91194969] mean value: 0.8443213542946248 key: test_recall value: [1. 1. 0.94117647 0.58823529 0.82352941 1. 0.88235294 0.70588235 1. 0.76470588] mean value: 0.8705882352941178 key: train_recall value: [1. 1. 0.86842105 0.60526316 0.98684211 1. 0.96710526 0.68421053 1. 0.95394737] mean value: 0.906578947368421 key: test_accuracy value: [0.61764706 0.82352941 0.79411765 0.79411765 0.73529412 0.85294118 0.76470588 0.82352941 0.84848485 0.72727273] mean value: 0.7781639928698753 key: train_accuracy value: [0.61842105 0.83223684 0.91447368 0.79605263 0.96381579 0.90131579 0.87828947 0.83223684 0.79344262 0.93114754] mean value: 0.8461432269197584 key: test_roc_auc value: [0.61764706 0.82352941 0.79411765 0.79411765 0.73529412 0.85294118 0.76470588 0.82352941 0.85294118 0.72610294] mean value: 0.7784926470588236 key: train_roc_auc value: [0.61842105 0.83223684 0.91447368 0.79605263 0.96381579 0.90131579 0.87828947 0.83223684 0.79276316 0.93122205] mean value: 0.8460827313381494 key: test_jcc value: [0.56666667 0.73913043 0.69565217 0.58823529 0.60869565 0.77272727 0.65217391 0.66666667 0.76190476 0.59090909] mean value: 0.664276192690515 key: train_jcc value: [0.56716418 0.74876847 0.83544304 0.5974026 0.93167702 0.83516484 0.79891304 0.67096774 0.70833333 0.87349398] mean value: 0.7567328235837231 MCC on Blind test: -0.08 MCC on Training: 0.59 Running classifier: 24 Model_name: XGBoost Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.11274195 0.09732723 0.07411265 0.07084489 0.074857 0.07602954 0.07695174 0.07398725 0.07294512 0.07340217] mean value: 0.08031995296478271 key: score_time value: [0.01166415 0.01181769 0.01147008 0.01068783 0.01065779 0.01149845 0.01070571 0.01074958 0.01088834 0.01097846] mean value: 0.011111807823181153 key: test_mcc value: [0.94280904 1. 0.88852332 0.88852332 0.73854895 0.88852332 0.94280904 0.94280904 0.83276554 0.88561489] mean value: 0.8950926450521759 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.97142857 1. 0.94444444 0.94444444 0.87179487 0.94444444 0.97142857 0.97142857 0.91428571 0.9375 ] mean value: 0.9471199633699634 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:463: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_CV['source_data'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:490: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_BT['source_data'] = 'BT' [0.94444444 1. 0.89473684 0.89473684 0.77272727 0.89473684 0.94444444 0.94444444 0.84210526 1. ] mean value: 0.9132376395534291 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.88235294] mean value: 0.9882352941176471 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.97058824 1. 0.94117647 0.94117647 0.85294118 0.94117647 0.97058824 0.97058824 0.90909091 0.93939394] mean value: 0.9436720142602495 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.97058824 1. 0.94117647 0.94117647 0.85294118 0.94117647 0.97058824 0.97058824 0.91176471 0.94117647] mean value: 0.9441176470588235 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.94444444 1. 0.89473684 0.89473684 0.77272727 0.89473684 0.94444444 0.94444444 0.84210526 0.88235294] mean value: 0.9014729336710762 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.36 MCC on Training: 0.9 Extracting tts_split_name: 70_30 Total cols in each df: CV df: 8 metaDF: 17 Adding column: Model_name Total cols in bts df: BT_df: 8 First proceeding to rowbind CV and BT dfs: Final output should have: 25 columns Combinig 2 using pd.concat by row ~ rowbind Checking Dims of df to combine: Dim of CV: (24, 8) Dim of BT: (24, 8) 8 Number of Common columns: 8 These are: ['source_data', 'Precision', 'JCC', 'MCC', 'Recall', 'Accuracy', 'F1', 'ROC_AUC'] Concatenating dfs with different resampling methods [WF]: Split type: 70_30 No. of dfs combining: 2 PASS: 2 dfs successfully combined nrows in combined_df_wf: 48 ncols in combined_df_wf: 8 PASS: proceeding to merge metadata with CV and BT dfs Adding column: Model_name ========================================================= SUCCESS: Ran multiple classifiers ======================================================= Input params: Dim of input df: (858, 175) Data type to split: complete Split type: 70_30 target colname: dst_mode oversampling enabled PASS: x_features has no target variable and no dst column Dropped cols: 2 These were: dst_mode and dst No. of cols in input df: 175 No.of cols dropped: 2 No. of columns for x_features: 173 ------------------------------------------------------------- Successfully generated training and test data: Data used: complete Split type: 70_30 Total no. of input features: 173 --------No. of numerical features: 167 --------No. of categorical features: 6 =========================== Resampling: NONE Baseline =========================== Total data size: 858 Train data size: (574, 173) y_train numbers: Counter({0: 489, 1: 85}) Test data size: (284, 173) y_test_numbers: Counter({0: 242, 1: 42}) y_train ratio: 5.752941176470588 y_test ratio: 5.761904761904762 ------------------------------------------------------------- Simple Random OverSampling Counter({0: 489, 1: 489}) (978, 173) Simple Random UnderSampling Counter({0: 85, 1: 85}) (170, 173) Simple Combined Over and UnderSampling Counter({0: 489, 1: 489}) (978, 173) SMOTE_NC OverSampling Counter({0: 489, 1: 489}) (978, 173) Generated Resampled data as below: ================================= Resampling: Random oversampling ================================ Train data size: (978, 173) y_train numbers: 978 y_train ratio: 1.0 y_test ratio: 5.761904761904762 ================================ Resampling: Random underampling ================================ Train data size: (170, 173) y_train numbers: 170 y_train ratio: 1.0 y_test ratio: 5.761904761904762 ================================ Resampling:Combined (over+under) ================================ Train data size: (978, 173) y_train numbers: 978 y_train ratio: 1.0 y_test ratio: 5.761904761904762 ============================== Resampling: Smote NC ============================== Train data size: (978, 173) y_train numbers: 978 y_train ratio: 1.0 y_test ratio: 5.761904761904762 ------------------------------------------------------------- ============================================================== Running several classification models (n): 24 List of models: ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('Gaussian NB', GaussianNB()) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('K-Nearest Neighbors', KNeighborsClassifier()) ('LDA', LinearDiscriminantAnalysis()) ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(cv=3, random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Multinomial', MultinomialNB()) ('Naive Bayes', BernoulliNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=12, random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Random Forest', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42)) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=3)) ('SVC', SVC(random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=12, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ================================================================ Running classifier: 1 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.19268703 0.19989824 0.19213915 0.19212198 0.19584274 0.19765472 0.19911075 0.18795371 0.18431735 0.18368292] mean value: 0.19254086017608643 key: score_time value: [0.01627684 0.01655078 0.01637197 0.01660037 0.01590633 0.01668167 0.01580739 0.01528883 0.01513982 0.01511669] mean value: 0.0159740686416626 key: test_mcc value: [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.4s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.4s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... àp@ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþÿÿÿÿÿÿÿÀ¦€p@34! ‰á?rÇqÇÑ?@ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþÿÿÿÿÿÿÿÀð?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþÿÿÿÿÿÿÿÀ@6; €K†à?ÀÅ?'P@7:P#¸â?ÇqÇqÜ? .@89ðÓOß?rÇqÇÑ?@ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþÿÿÿÿÿÿÿÀð?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþÿÿÿÿÿÿÿÀ@ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþÿÿÿÿÿÿÿÀ"@<?¥ÀÝ?@»E²x¤?€H@=>°Ÿ~Ú?à?@ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþÿÿÿÿÿÿÿÀð?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþÿÿÿÿÿÿÿÀð?ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþÿÿÿÿÿÿÿÀ€G@AB#¾Ú8[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.4s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.4s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... =@ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþÿÿÿÿÿÿÿÀ 4@ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþÿÿÿÿÿÿÿÀ"@ñà´Y%VÀ<|ÜÿÿÿÿÿÿÿÿþÿÿÿÿÿÿÿÀ@GH[°¡¼æ?†ÊS—Ûß? .@ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþÿÿÿÿÿÿÿÀ @ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþÿÿÿÿÿÿÿÀ P Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.8s remaining: 3.6s Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.8s remaining: 3.7s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.9s remaining: 3.8s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.9s remaining: 0.6s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.9s remaining: 0.6s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.9s remaining: 0.6s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 2.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 2.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 2.0s remaining: 4.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 2.0s remaining: 4.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 2.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 2.0s remaining: 4.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 2.0s remaining: 4.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 2.1s remaining: 0.7s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 2.1s remaining: 4.2s Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 2.1s remaining: 0.7s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 2.1s remaining: 0.7s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 2.1s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 2.1s remaining: 4.2s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 2.1s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 2.1s remaining: 0.7s Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 2.1s remaining: 4.3s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 2.2s remaining: 0.7s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 2.2s remaining: 0.7s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 2.1s remaining: 0.7s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 2.2s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 2.2s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 2.2s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 2.2s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 2.2s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [ 0.27974967 0.16714566 0.07128145 0.42592517 0.15658528 0.02598596 0.12755102 -0.07705141 -0.07705141 0.43275671] mean value: 0.15328780817479778 key: train_mcc value: [0.75237302 0.77144444 0.79565281 0.67724002 0.73739489 0.70907016 0.72826459 0.73625996 0.75641804 0.78684034] mean value: 0.745095827802223 key: test_fscore value: [0.375 0.26666667 0.15384615 0.5 0.26666667 0.14285714 0.25 0. 0. 0.52631579] mean value: 0.24813524195103137 key: train_fscore value: [0.7761194 0.79710145 0.80916031 0.6984127 0.76119403 0.72868217 0.7518797 0.75384615 0.7826087 0.8030303 ] mean value: 0.7662034908186779 key: test_precision value: [0.42857143 0.33333333 0.25 0.57142857 0.28571429 0.16666667 0.25 0. 0. 0.5 ] mean value: 0.2785714285714286 key: train_precision value: [0.89655172 0.88709677 0.96363636 0.88 0.89473684 0.90384615 0.89285714 0.9245283 0.8852459 0.94642857] mean value: 0.907492777573111 key: test_recall value: [0.33333333 0.22222222 0.11111111 0.44444444 0.25 0.125 0.25 0. 0. 0.55555556] mean value: 0.22916666666666669 key: train_recall value: [0.68421053 0.72368421 0.69736842 0.57894737 0.66233766 0.61038961 0.64935065 0.63636364 0.7012987 0.69736842] mean value: 0.664131920710868 key: test_accuracy value: [0.82758621 0.81034483 0.81034483 0.86206897 0.80701754 0.78947368 0.78947368 0.8245614 0.8245614 0.84210526] mean value: 0.8187537810042347 key: train_accuracy value: [0.94186047 0.94573643 0.95155039 0.92635659 0.93810445 0.93230174 0.93617021 0.93810445 0.94197292 0.94970986] mean value: 0.9401867512332629 key: test_roc_auc value: [0.62585034 0.57029478 0.52494331 0.69160998 0.57397959 0.51147959 0.56377551 0.47959184 0.47959184 0.72569444] mean value: 0.5746811224489796 key: train_roc_auc value: [0.83528708 0.85388756 0.84641148 0.7826555 0.82435065 0.79951299 0.81785714 0.81363636 0.84269481 0.84528285] mean value: 0.8261576424828304 key: test_jcc value: [0.23076923 0.15384615 0.08333333 0.33333333 0.15384615 0.07692308 0.14285714 0. 0. 0.35714286] mean value: 0.15320512820512822 key: train_jcc value: [0.63414634 0.6626506 0.67948718 0.53658537 0.61445783 0.57317073 0.60240964 0.60493827 0.64285714 0.67088608] mean value: 0.6221589181212175 MCC on Blind test: 0.33 MCC on Training: 0.15 Running classifier: 2 Model_name: Bagging Classifier Model func: BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3))]) key: fit_time value: [0.33702111 0.32559562 0.35045433 0.36850095 0.33423877 0.35723686 0.32405591 0.30851126 0.33737111 0.34519529] mean value: 0.3388181209564209 key: score_time value: [0.05067587 0.05246997 0.0701673 0.05940223 0.07455063 0.05922937 0.0367651 0.07152176 0.06794882 0.06676531] mean value: 0.06094963550567627 key: test_mcc value: [0.44095855 0.25920526 0.16714566 0.6350529 0.35714286 0.48217405 0.4719399 0.33071891 0.33071891 0.37596031] mean value: 0.3851017319931905 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.36363636 0.30769231 0.26666667 0.61538462 0.36363636 0.5 0.4 0.22222222 0.22222222 0.42857143] mean value: 0.369003219003219 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.5 0.33333333 1. 0.66666667 0.75 1. 1. 1. 0.6 ] mean value: 0.7849999999999999 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.22222222 0.22222222 0.22222222 0.44444444 0.25 0.375 0.25 0.125 0.125 0.33333333] mean value: 0.2569444444444445 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.87931034 0.84482759 0.81034483 0.9137931 0.87719298 0.89473684 0.89473684 0.87719298 0.87719298 0.85964912] mean value: 0.872897761645493 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.61111111 0.59070295 0.57029478 0.72222222 0.61479592 0.67729592 0.625 0.5625 0.5625 0.64583333] mean value: 0.6182256235827664 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.22222222 0.18181818 0.15384615 0.44444444 0.22222222 0.33333333 0.25 0.125 0.125 0.27272727] mean value: 0.23306138306138308 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.41 MCC on Training: 0.39 Running classifier: 3 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.03582621 0.03917551 0.03514242 0.03686857 0.03649306 0.03913903 0.0388937 0.0436933 0.04152656 0.04220653] mean value: 0.038896489143371585 key: score_time value: [0.00966287 0.00926375 0.00951767 0.009413 0.00953555 0.00962591 0.0097723 0.01034307 0.00978637 0.01224947] mean value: 0.009916996955871582 key: test_mcc value: [ 0.13357416 0.21088435 0.09209405 0.54701518 0.31047082 0.41836735 0.24057489 -0.1110041 0.284448 0.37596031] mean value: 0.2502385009242752 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.25 0.33333333 0.26086957 0.57142857 0.4 0.5 0.35294118 0. 0.33333333 0.42857143] mean value: 0.3430477408354646 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.28571429 0.33333333 0.21428571 0.8 0.42857143 0.5 0.33333333 0. 0.5 0.6 ] mean value: 0.3995238095238095 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.22222222 0.33333333 0.33333333 0.44444444 0.375 0.5 0.375 0. 0.25 0.33333333] mean value: 0.31666666666666665 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.79310345 0.79310345 0.70689655 0.89655172 0.84210526 0.85964912 0.80701754 0.78947368 0.85964912 0.85964912] mean value: 0.8207199032062915 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.5600907 0.60544218 0.55442177 0.71201814 0.64668367 0.70918367 0.62627551 0.45918367 0.60459184 0.64583333] mean value: 0.6123724489795919 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.14285714 0.2 0.15 0.4 0.25 0.33333333 0.21428571 0. 0.2 0.27272727] mean value: 0.21632034632034633 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.3 MCC on Training: 0.25 Running classifier: 4 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.01083946 0.0103972 0.01105642 0.01110077 0.01143241 0.01122808 0.01114702 0.01046062 0.01042604 0.0114727 ] mean value: 0.010956072807312011 key: score_time value: [0.00898027 0.00898767 0.0089674 0.00976229 0.00957513 0.00889492 0.00921035 0.00946784 0.00924301 0.00958133] mean value: 0.009267020225524902 key: test_mcc value: [ 0.20769025 0.22641261 0.05510834 -0.03333333 0.19056371 0.02598596 0.18636305 -0.06960548 0.15658528 0.1539981 ] mean value: 0.10997684891432062 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.28571429 0.36363636 0.24 0.11764706 0.28571429 0.14285714 0.31578947 0.10526316 0.26666667 0.3 ] mean value: 0.24232884349912212 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.4 0.30769231 0.1875 0.125 0.33333333 0.16666667 0.27272727 0.09090909 0.28571429 0.27272727] mean value: 0.244227022977023 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.22222222 0.44444444 0.33333333 0.11111111 0.25 0.125 0.375 0.125 0.25 0.33333333] mean value: 0.2569444444444445 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.82758621 0.75862069 0.67241379 0.74137931 0.8245614 0.78947368 0.77192982 0.70175439 0.80701754 0.75438596] mean value: 0.7649122807017543 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.58049887 0.63038549 0.53401361 0.48412698 0.58418367 0.51147959 0.60586735 0.46045918 0.57397959 0.58333333] mean value: 0.5548327664399093 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.16666667 0.22222222 0.13636364 0.0625 0.16666667 0.07692308 0.1875 0.05555556 0.15384615 0.17647059] mean value: 0.14047145664792723 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.12 MCC on Training: 0.11 Running classifier: 5 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.12884307 0.13111997 0.12910461 0.13170218 0.13768005 0.13793373 0.13799858 0.14092112 0.14390969 0.14129472] mean value: 0.13605077266693116 key: score_time value: [0.01787329 0.01903915 0.01813817 0.02653885 0.01814628 0.01917529 0.01944494 0.01963902 0.02025938 0.02034736] mean value: 0.019860172271728517 key: test_mcc value: [-0.08099239 0.11492127 0.20769025 0.17998308 -0.07705141 0.19744425 0.19744425 -0.05399492 0.33071891 0.44609152] mean value: 0.1462254805960527 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0. 0.16666667 0.28571429 0.18181818 0. 0.2 0.2 0. 0.22222222 0.46153846] mean value: 0.17179598179598182 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0. 0.33333333 0.4 0.5 0. 0.5 0.5 0. 1. 0.75 ] mean value: 0.3983333333333333 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0. 0.11111111 0.22222222 0.11111111 0. 0.125 0.125 0. 0.125 0.33333333] mean value: 0.11527777777777777 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.81034483 0.82758621 0.82758621 0.84482759 0.8245614 0.85964912 0.85964912 0.84210526 0.87719298 0.87719298] mean value: 0.845069570477919 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.47959184 0.53514739 0.58049887 0.54535147 0.47959184 0.55229592 0.55229592 0.48979592 0.5625 0.65625 ] mean value: 0.5433319160997733 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0. 0.09090909 0.16666667 0.1 0. 0.11111111 0.11111111 0. 0.125 0.3 ] mean value: 0.10047979797979797 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.13 MCC on Training: 0.15 Running classifier: 6 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.73046398 0.7285254 0.72884488 0.72569156 0.73967242 0.73600721 0.74949622 0.75964022 0.74119568 0.75519514] mean value: 0.7394732713699341 key: score_time value: [0.00932312 0.00928521 0.00942016 0.00923061 0.0096519 0.00938678 0.01060343 0.01039481 0.01022792 0.00946975] mean value: 0.009699368476867675 key: test_mcc value: [0.37735271 0.32993527 0.13357416 0.44712908 0.35714286 0.35714286 0.19056371 0. 0.33071891 0.32179795] mean value: 0.2845357511126501 key: train_mcc value: [0.94534499 0.95323753 0.9611003 0.9214786 0.94596754 0.9615397 0.94596754 0.94596754 0.94596754 0.93744066] mean value: 0.9464011922843818 key: test_fscore value: [0.42857143 0.33333333 0.25 0.46153846 0.36363636 0.36363636 0.28571429 0. 0.22222222 0.4 ] mean value: 0.3108652458652459 key: train_fscore value: [0.95172414 0.95890411 0.96598639 0.92957746 0.95238095 0.96644295 0.95238095 0.95238095 0.95238095 0.94444444] mean value: 0.952660331385502 key: test_precision value: [0.6 0.66666667 0.28571429 0.75 0.66666667 0.66666667 0.33333333 0. 1. 0.5 ] mean value: 0.5469047619047618 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.33333333 0.22222222 0.22222222 0.33333333 0.25 0.25 0.25 0. 0.125 0.33333333] mean value: 0.23194444444444445 key: train_recall value: [0.90789474 0.92105263 0.93421053 0.86842105 0.90909091 0.93506494 0.90909091 0.90909091 0.90909091 0.89473684] mean value: 0.9097744360902256 key: test_accuracy value: [0.86206897 0.86206897 0.79310345 0.87931034 0.87719298 0.87719298 0.8245614 0.85964912 0.87719298 0.84210526] mean value: 0.8554446460980035 key: train_accuracy value: [0.98643411 0.98837209 0.99031008 0.98062016 0.98646035 0.99032882 0.98646035 0.98646035 0.98646035 0.98452611] mean value: 0.9866432759060171 key: test_roc_auc value: [0.6462585 0.60090703 0.5600907 0.65646259 0.61479592 0.61479592 0.58418367 0.5 0.5625 0.63541667] mean value: 0.5975410997732427 key: train_roc_auc value: [0.95394737 0.96052632 0.96710526 0.93421053 0.95454545 0.96753247 0.95454545 0.95454545 0.95454545 0.94736842] mean value: 0.9548872180451129 key: test_jcc value: [0.27272727 0.2 0.14285714 0.3 0.22222222 0.22222222 0.16666667 0. 0.125 0.25 ] mean value: 0.19016955266955266 key: train_jcc value: [0.90789474 0.92105263 0.93421053 0.86842105 0.90909091 0.93506494 0.90909091 0.90909091 0.90909091 0.89473684] mean value: 0.9097744360902256 MCC on Blind test: 0.34 MCC on Training: 0.28 Running classifier: 7 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.01126313 0.0108397 0.01090932 0.01116776 0.01006222 0.01482034 0.01560926 0.01149464 0.00998521 0.01015162] mean value: 0.01163032054901123 key: score_time value: [0.00949216 0.00895286 0.00960231 0.00947428 0.00889754 0.01462722 0.01100802 0.00917602 0.00884056 0.00879431] mean value: 0.009886527061462402 key: test_mcc value: [0.25381038 0.36887783 0.24710691 0.20337437 0.16301267 0.28690229 0.35612694 0.03571429 0.24057489 0.18570841] mean value: 0.23412089532039246 key: train_mcc value: [0.34716196 0.34546701 0.34810911 0.32443737 0.39821257 0.3238882 0.35737337 0.38211056 0.33573864 0.33640551] mean value: 0.3498904285660406 key: test_fscore value: [0.38709677 0.47619048 0.38461538 0.34782609 0.3 0.4 0.45454545 0.22222222 0.35294118 0.34482759] mean value: 0.3670265161401092 key: train_fscore value: [0.45283019 0.45263158 0.45410628 0.43518519 0.4950495 0.43478261 0.46226415 0.48113208 0.44255319 0.44247788] mean value: 0.4553012640661834 key: test_precision value: [0.27272727 0.41666667 0.29411765 0.28571429 0.25 0.33333333 0.35714286 0.15789474 0.33333333 0.25 ] mean value: 0.29509301328186777 key: train_precision value: [0.35294118 0.37719298 0.35877863 0.33571429 0.4 0.32679739 0.36296296 0.37777778 0.32911392 0.33333333] mean value: 0.35546124543408353 key: test_recall value: [0.66666667 0.55555556 0.55555556 0.44444444 0.375 0.5 0.625 0.375 0.375 0.55555556] mean value: 0.5027777777777778 key: train_recall value: [0.63157895 0.56578947 0.61842105 0.61842105 0.64935065 0.64935065 0.63636364 0.66233766 0.67532468 0.65789474] mean value: 0.6364832535885168 key: test_accuracy value: [0.67241379 0.81034483 0.72413793 0.74137931 0.75438596 0.78947368 0.78947368 0.63157895 0.80701754 0.66666667] mean value: 0.7386872353297036 key: train_accuracy value: [0.7751938 0.79844961 0.78100775 0.76356589 0.80270793 0.74854932 0.7794971 0.78723404 0.74661509 0.75628627] mean value: 0.7739106802812888 key: test_roc_auc value: [0.67006803 0.70634921 0.6553288 0.62018141 0.59566327 0.66836735 0.72066327 0.52423469 0.62627551 0.62152778] mean value: 0.6408659297052155 key: train_roc_auc value: [0.71578947 0.70221292 0.71375598 0.70352871 0.73944805 0.70762987 0.72045455 0.73571429 0.71720779 0.71556868] mean value: 0.7171310310408054 key: test_jcc value: [0.24 0.3125 0.23809524 0.21052632 0.17647059 0.25 0.29411765 0.125 0.21428571 0.20833333] mean value: 0.22693288367978776 key: train_jcc value: [0.29268293 0.29251701 0.29375 0.27810651 0.32894737 0.27777778 0.3006135 0.31677019 0.28415301 0.28409091] mean value: 0.2949409186529869 MCC on Blind test: 0.29 MCC on Training: 0.23 Running classifier: 8 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.17333364 0.20338058 0.18404293 0.19854426 0.19112158 0.24198532 0.20272541 0.20854902 0.20405698 0.19948483] mean value: 0.20072245597839355 key: score_time value: [0.02913189 0.03243136 0.02953815 0.03271079 0.02949882 0.03107095 0.03022003 0.02573848 0.03292751 0.02938151] mean value: 0.030264949798583983 key: test_mcc value: [ 0. 0.44095855 0.17998308 0. -0.05399492 0.33071891 -0.05399492 -0.05399492 0. 0. ] mean value: 0.07896757739657068 key: train_mcc value: [0.62893092 0.62893092 0.60831639 0.64905429 0.63423013 0.62418778 0.63423013 0.63423013 0.63423013 0.63911409] mean value: 0.6315454911866064 key: test_fscore value: [0. 0.36363636 0.18181818 0. 0. 0.22222222 0. 0. 0. 0. ] mean value: 0.07676767676767676 key: train_fscore value: [0.60550459 0.60550459 0.57943925 0.63063063 0.61261261 0.6 0.61261261 0.61261261 0.61261261 0.61818182] mean value: 0.6089711325911275 key: test_precision value: [0. 1. 0.5 0. 0. 1. 0. 0. 0. 0. ] mean value: 0.25 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0. 0.22222222 0.11111111 0. 0. 0.125 0. 0. 0. 0. ] mean value: 0.04583333333333333 key: train_recall value: [0.43421053 0.43421053 0.40789474 0.46052632 0.44155844 0.42857143 0.44155844 0.44155844 0.44155844 0.44736842] mean value: 0.4379015721120984 key: test_accuracy value: [0.84482759 0.87931034 0.84482759 0.84482759 0.84210526 0.87719298 0.84210526 0.84210526 0.85964912 0.84210526] mean value: 0.8519056261343014 key: train_accuracy value: [0.91666667 0.91666667 0.9127907 0.92054264 0.91682785 0.91489362 0.91682785 0.91682785 0.91682785 0.91876209] mean value: 0.9167633784655062 key: test_roc_auc value: [0.5 0.61111111 0.54535147 0.5 0.48979592 0.5625 0.48979592 0.48979592 0.5 0.5 ] mean value: 0.5188350340136054 key: train_roc_auc value: [0.71710526 0.71710526 0.70394737 0.73026316 0.72077922 0.71428571 0.72077922 0.72077922 0.72077922 0.72368421] mean value: 0.7189507860560492 key: test_jcc value: [0. 0.22222222 0.1 0. 0. 0.125 0. 0. 0. 0. ] mean value: 0.04472222222222222 key: train_jcc value: [0.43421053 0.43421053 0.40789474 0.46052632 0.44155844 0.42857143 0.44155844 0.44155844 0.44155844 0.44736842] mean value: 0.4379015721120984 MCC on Blind test: 0.08 MCC on Training: 0.08 Running classifier: 9 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.01267338 0.00967622 0.01156664 0.01074171 0.00958943 0.01107669 0.01067901 0.01096272 0.01121664 0.01109529] mean value: 0.010927772521972657 key: score_time value: [0.03805041 0.01907659 0.01927376 0.01651502 0.01472497 0.01409411 0.0130415 0.01512432 0.01408482 0.01523161] mean value: 0.017921710014343263 key: test_mcc value: [ 0. 0.17998308 0.44095855 -0.05676567 0. 0.33071891 0.19744425 -0.05399492 0. 0.17890661] mean value: 0.12172508082199673 key: train_mcc value: [0.30084326 0.24153755 0.21925077 0.28213405 0.29632751 0.26024618 0.25566999 0.30170365 0.25682567 0.28220909] mean value: 0.26967477206823054 key: test_fscore value: [0. 0.18181818 0.36363636 0. 0. 0.22222222 0.2 0. 0. 0.18181818] mean value: 0.11494949494949495 key: train_fscore value: [0.24444444 0.18390805 0.1627907 0.2247191 0.2247191 0.20224719 0.16470588 0.25806452 0.18390805 0.2247191 ] mean value: 0.20742261269368817 key: test_precision value: [0. 0.5 1. 0. 0. 1. 0.5 0. 0. 0.5] mean value: 0.35 key: train_precision value: [0.78571429 0.72727273 0.7 0.76923077 0.83333333 0.75 0.875 0.75 0.8 0.76923077] mean value: 0.7759781884781886 key: test_recall value: [0. 0.11111111 0.22222222 0. 0. 0.125 0.125 0. 0. 0.11111111] mean value: 0.06944444444444445 key: train_recall value: [0.14473684 0.10526316 0.09210526 0.13157895 0.12987013 0.11688312 0.09090909 0.15584416 0.1038961 0.13157895] mean value: 0.12026657552973341 key: test_accuracy value: [0.84482759 0.84482759 0.87931034 0.82758621 0.85964912 0.87719298 0.85964912 0.84210526 0.85964912 0.84210526] mean value: 0.8536902601330911 key: train_accuracy value: [0.86821705 0.8624031 0.86046512 0.86627907 0.86653772 0.86266925 0.86266925 0.86653772 0.86266925 0.86653772] mean value: 0.8644985230833822 key: test_roc_auc value: [0.5 0.54535147 0.61111111 0.48979592 0.5 0.5625 0.55229592 0.48979592 0.5 0.54513889] mean value: 0.5295989229024943 key: train_roc_auc value: [0.56895933 0.54922249 0.54264354 0.56238038 0.56266234 0.55503247 0.54431818 0.57337662 0.54967532 0.56238811] mean value: 0.5570658789831722 key: test_jcc value: [0. 0.1 0.22222222 0. 0. 0.125 0.11111111 0. 0. 0.1 ] mean value: 0.06583333333333333 key: train_jcc value: [0.13924051 0.10126582 0.08860759 0.12658228 0.12658228 0.1125 0.08974359 0.14814815 0.10126582 0.12658228] mean value: 0.11605183201702189 MCC on Blind test: 0.02 MCC on Training: 0.12 Running classifier: 10 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.0428226 0.0681777 0.07777357 0.05137753 0.04229426 0.04235625 0.0589354 0.04197264 0.08408308 0.06131124] mean value: 0.05711042881011963 key: score_time value: [0.01931572 0.02038026 0.02332759 0.01338458 0.01323271 0.01368403 0.01330423 0.01293707 0.02872777 0.01295519] mean value: 0.01712491512298584 key: test_mcc value: [ 0.05651106 0.21088435 0.21088435 0.07936508 0.08672195 0.34480198 0.19056371 -0.15118579 0.284448 0.43275671] mean value: 0.17457514051144074 key: train_mcc value: [0.58750355 0.58750355 0.56189659 0.56189659 0.51382 0.53597001 0.53923801 0.60069195 0.55395587 0.49680654] mean value: 0.5539282660349645 key: test_fscore value: [0.21052632 0.33333333 0.33333333 0.22222222 0.16666667 0.44444444 0.28571429 0. 0.33333333 0.52631579] mean value: 0.2855889724310777 key: train_fscore value: [0.625 0.625 0.60465116 0.60465116 0.55555556 0.58646617 0.58461538 0.63565891 0.59375 0.546875 ] mean value: 0.5962223345894552 key: test_precision value: [0.2 0.33333333 0.33333333 0.22222222 0.25 0.4 0.33333333 0. 0.5 0.5 ] mean value: 0.30722222222222223 key: train_precision value: [0.76923077 0.76923077 0.73584906 0.73584906 0.71428571 0.69642857 0.71698113 0.78846154 0.74509804 0.67307692] mean value: 0.7344491570212991 key: test_recall value: [0.22222222 0.33333333 0.33333333 0.22222222 0.125 0.5 0.25 0. 0.25 0.55555556] mean value: 0.2791666666666667 key: train_recall value: [0.52631579 0.52631579 0.51315789 0.51315789 0.45454545 0.50649351 0.49350649 0.53246753 0.49350649 0.46052632] mean value: 0.5019993164730007 key: test_accuracy value: [0.74137931 0.79310345 0.79310345 0.75862069 0.8245614 0.8245614 0.8245614 0.73684211 0.85964912 0.84210526] mean value: 0.799848759830611 key: train_accuracy value: [0.90697674 0.90697674 0.90116279 0.90116279 0.89168279 0.89361702 0.89555126 0.90909091 0.89941973 0.88781431] mean value: 0.899345508524133 key: test_roc_auc value: [0.52947846 0.60544218 0.60544218 0.53968254 0.53188776 0.68877551 0.58418367 0.42857143 0.60459184 0.72569444] mean value: 0.5843750000000001 key: train_roc_auc value: [0.74952153 0.74952153 0.74066986 0.74066986 0.71136364 0.73392857 0.72970779 0.75373377 0.73198052 0.71098878] mean value: 0.7352085842311405 key: test_jcc value: [0.11764706 0.2 0.2 0.125 0.09090909 0.28571429 0.16666667 0. 0.2 0.35714286] mean value: 0.174307995925643 key: train_jcc value: [0.45454545 0.45454545 0.43333333 0.43333333 0.38461538 0.41489362 0.41304348 0.46590909 0.42222222 0.37634409] mean value: 0.42527854548079247 MCC on Blind test: 0.17 MCC on Training: 0.17 Running classifier: 11 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.02958369 0.04241347 0.06783772 0.04222512 0.06806588 0.06484795 0.03977489 0.04006219 0.0403955 0.0396421 ] mean value: 0.047484850883483885 key: score_time value: [0.01214457 0.01231003 0.01217914 0.01249909 0.02333808 0.01669908 0.01223922 0.01252937 0.01254439 0.01220322] mean value: 0.013868618011474609 key: test_mcc value: [-0.08099239 0.32993527 0.07128145 0.30905755 -0.07705141 0.19744425 0.19744425 -0.07705141 0.33071891 0.1134023 ] mean value: 0.13141887592162055 key: train_mcc value: [0.37243492 0.34150842 0.30186218 0.3131775 0.34667399 0.29922054 0.3615688 0.34135513 0.35769615 0.3725263 ] mean value: 0.34080239314628724 key: test_fscore value: [0. 0.33333333 0.15384615 0.2 0. 0.2 0.2 0. 0.22222222 0.16666667] mean value: 0.14760683760683763 key: train_fscore value: [0.34693878 0.3125 0.28865979 0.29166667 0.33663366 0.28571429 0.35294118 0.34615385 0.34 0.34693878] mean value: 0.32481469832065646 key: test_precision value: [0. 0.66666667 0.25 1. 0. 0.5 0.5 0. 1. 0.33333333] mean value: 0.425 key: train_precision value: [0.77272727 0.75 0.66666667 0.7 0.70833333 0.66666667 0.72 0.66666667 0.73913043 0.77272727] mean value: 0.7162918313570488 key: test_recall value: [0. 0.22222222 0.11111111 0.11111111 0. 0.125 0.125 0. 0.125 0.11111111] mean value: 0.09305555555555556 key: train_recall value: [0.22368421 0.19736842 0.18421053 0.18421053 0.22077922 0.18181818 0.23376623 0.23376623 0.22077922 0.22368421] mean value: 0.2104066985645933 key: test_accuracy value: [0.81034483 0.86206897 0.81034483 0.86206897 0.8245614 0.85964912 0.85964912 0.8245614 0.87719298 0.8245614 ] mean value: 0.8415003024803388 key: train_accuracy value: [0.87596899 0.87209302 0.86627907 0.86821705 0.87040619 0.86460348 0.87234043 0.86847195 0.87234043 0.8762089 ] mean value: 0.8706929512842428 key: test_roc_auc value: [0.47959184 0.60090703 0.52494331 0.55555556 0.47959184 0.55229592 0.55229592 0.47959184 0.5625 0.53472222] mean value: 0.5321995464852607 key: train_roc_auc value: [0.60616029 0.59300239 0.58415072 0.58528708 0.60243506 0.58295455 0.60892857 0.60665584 0.60357143 0.60617317] mean value: 0.597931910403715 key: test_jcc value: [0. 0.2 0.08333333 0.11111111 0. 0.11111111 0.11111111 0. 0.125 0.09090909] mean value: 0.08325757575757577 key: train_jcc value: [0.20987654 0.18518519 0.1686747 0.17073171 0.20238095 0.16666667 0.21428571 0.20930233 0.20481928 0.20987654] mean value: 0.19417996137403545 MCC on Blind test: 0.21 MCC on Training: 0.13 Running classifier: 12 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(cv=3, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(cv=3, random_state=42))]) key: fit_time value: [0.47326016 0.46630788 0.47561121 0.61593246 0.59409618 0.49347067 0.48221302 0.60630131 0.50549054 0.48207903] mean value: 0.5194762468338012 key: score_time value: [0.01263881 0.01269197 0.0126555 0.01271152 0.0121479 0.01278114 0.01255989 0.01263118 0.01268506 0.01270223] mean value: 0.01262052059173584 key: test_mcc value: [ 0. 0. 0. 0. -0.07705141 0. 0. 0. 0. 0. ] mean value: -0.007705141285294195 key: train_mcc value: [0. 0. 0. 0. 0.44354775 0. 0. 0. 0. 0. ] mean value: 0.0443547749210373 key: test_fscore value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_fscore value: [0. 0. 0. 0. 0.44444444 0. 0. 0. 0. 0. ] mean value: 0.044444444444444446 key: test_precision value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_precision value: [0. 0. 0. 0. 0.77419355 0. 0. 0. 0. 0. ] mean value: 0.07741935483870968 key: test_recall value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_recall value: [0. 0. 0. 0. 0.31168831 0. 0. 0. 0. 0. ] mean value: 0.03116883116883117 key: test_accuracy value: [0.84482759 0.84482759 0.84482759 0.84482759 0.8245614 0.85964912 0.85964912 0.85964912 0.85964912 0.84210526] mean value: 0.8484573502722323 key: train_accuracy value: [0.85271318 0.85271318 0.85271318 0.85271318 0.88394584 0.85106383 0.85106383 0.85106383 0.85106383 0.85299807] mean value: 0.8552051939483905 key: test_roc_auc value: [0.5 0.5 0.5 0.5 0.47959184 0.5 0.5 0.5 0.5 0.5 ] mean value: 0.4979591836734694 key: train_roc_auc value: [0.5 0.5 0.5 0.5 0.64788961 0.5 0.5 0.5 0.5 0.5 ] mean value: 0.514788961038961 key: test_jcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_jcc value: [0. 0. 0. 0. 0.28571429 0. 0. 0. 0. 0. ] mean value: 0.02857142857142857 MCC on Blind test: 0.0 MCC on Training: -0.01 Running classifier: 13 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [2.09115958 2.15175176 2.18207598 2.63452911 2.45700526 1.92617774 2.3686192 2.14489007 2.24329424 2.0742147 ] mean value: 2.2273717641830446 key: score_time value: [0.01266813 0.01324749 0.0128684 0.01477265 0.01486301 0.01272416 0.01269817 0.01268005 0.01240087 0.01268196] mean value: 0.013160490989685058 key: test_mcc value: [ 0.03802779 0.03802779 0.37735271 0.37735271 0.02598596 0.19056371 0.05324995 -0.07705141 0.284448 0.32179795] mean value: 0.1629755172692053 key: train_mcc value: [0.85562837 0.88100579 0.88049557 0.80549384 0.87423542 0.79010032 0.79938532 0.79860699 0.78954524 0.80479734] mean value: 0.8279294201650667 key: test_fscore value: [0.14285714 0.14285714 0.42857143 0.42857143 0.14285714 0.28571429 0.15384615 0. 0.33333333 0.4 ] mean value: 0.2458608058608059 key: train_fscore value: [0.87142857 0.89051095 0.89361702 0.8125 0.89041096 0.8115942 0.80620155 0.80916031 0.8030303 0.81538462] mean value: 0.8403838477558964 key: test_precision value: [0.2 0.2 0.6 0.6 0.16666667 0.33333333 0.2 0. 0.5 0.5 ] mean value: 0.32999999999999996 key: train_precision value: [0.953125 1. 0.96923077 1. 0.94202899 0.91803279 1. 0.98148148 0.96363636 0.98148148] mean value: 0.9709016868222587 key: test_recall value: [0.11111111 0.11111111 0.33333333 0.33333333 0.125 0.25 0.125 0. 0.25 0.33333333] mean value: 0.19722222222222222 key: train_recall value: [0.80263158 0.80263158 0.82894737 0.68421053 0.84415584 0.72727273 0.67532468 0.68831169 0.68831169 0.69736842] mean value: 0.7439166097060834 key: test_accuracy value: [0.79310345 0.79310345 0.86206897 0.86206897 0.78947368 0.8245614 0.80701754 0.8245614 0.85964912 0.84210526] mean value: 0.8257713248638836 key: train_accuracy value: [0.96511628 0.97093023 0.97093023 0.95348837 0.96905222 0.94970986 0.9516441 0.9516441 0.94970986 0.95357834] mean value: 0.9585803607575007 key: test_roc_auc value: [0.51473923 0.51473923 0.6462585 0.6462585 0.51147959 0.58418367 0.52168367 0.47959184 0.60459184 0.63541667] mean value: 0.5658942743764173 key: train_roc_auc value: [0.8979067 0.90131579 0.91220096 0.84210526 0.91753247 0.85795455 0.83766234 0.84301948 0.84188312 0.84755042] mean value: 0.8699131079864163 key: test_jcc value: [0.07692308 0.07692308 0.27272727 0.27272727 0.07692308 0.16666667 0.08333333 0. 0.2 0.25 ] mean value: 0.14762237762237762 key: train_jcc value: [0.7721519 0.80263158 0.80769231 0.68421053 0.80246914 0.68292683 0.67532468 0.67948718 0.67088608 0.68831169] mean value: 0.7266091895833315 MCC on Blind test: 0.22 MCC on Training: 0.16 Running classifier: 14 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.01440358 0.01413989 0.01194215 0.01125312 0.01236129 0.011621 0.01157856 0.01134777 0.0118556 0.01165605] mean value: 0.012215900421142577 key: score_time value: [0.01238966 0.01057839 0.01008058 0.01018 0.01033902 0.00981474 0.00982881 0.00974274 0.00978446 0.00988102] mean value: 0.010261940956115722 key: test_mcc value: [-0.08099239 0.17998308 -0.08099239 0.30905755 -0.07705141 -0.07705141 0.33071891 -0.0952381 0.33071891 0.17890661] mean value: 0.09180593768755803 key: train_mcc value: [0.25891886 0.19526254 0.23489673 0.11171737 0.16450722 0.15764292 0.13848443 0.16450722 0.06409372 0.17966224] mean value: 0.16696932554241803 key: test_fscore value: [0. 0.18181818 0. 0.2 0. 0. 0.22222222 0. 0.22222222 0.18181818] mean value: 0.10080808080808079 key: train_fscore value: [0.18604651 0.14117647 0.14457831 0.09411765 0.15384615 0.16842105 0.11494253 0.15384615 0.08791209 0.13953488] mean value: 0.13844218032205147 key: test_precision value: [0. 0.5 0. 1. 0. 0. 1. 0. 1. 0.5] mean value: 0.4 key: train_precision value: [0.8 0.66666667 0.85714286 0.44444444 0.5 0.44444444 0.5 0.5 0.28571429 0.6 ] mean value: 0.5598412698412698 key: test_recall value: [0. 0.11111111 0. 0.11111111 0. 0. 0.125 0. 0.125 0.11111111] mean value: 0.05833333333333333 key: train_recall value: [0.10526316 0.07894737 0.07894737 0.05263158 0.09090909 0.1038961 0.06493506 0.09090909 0.05194805 0.07894737] mean value: 0.07973342447026657 key: test_accuracy value: [0.81034483 0.84482759 0.81034483 0.86206897 0.8245614 0.8245614 0.87719298 0.80701754 0.87719298 0.84210526] mean value: 0.838021778584392 key: train_accuracy value: [0.86434109 0.85852713 0.8624031 0.85077519 0.85106383 0.84719536 0.85106383 0.85106383 0.83945841 0.85686654] mean value: 0.8532758310467365 key: test_roc_auc value: [0.47959184 0.54535147 0.47959184 0.55555556 0.47959184 0.47959184 0.5625 0.46938776 0.5625 0.54513889] mean value: 0.5158801020408164 key: train_roc_auc value: [0.55035885 0.53606459 0.53833732 0.52063397 0.5375 0.54058442 0.52678571 0.5375 0.51461039 0.53493854] mean value: 0.5337313793140861 key: test_jcc value: [0. 0.1 0. 0.11111111 0. 0. 0.125 0. 0.125 0.1 ] mean value: 0.05611111111111111 key: train_jcc value: [0.1025641 0.07594937 0.07792208 0.04938272 0.08333333 0.09195402 0.06097561 0.08333333 0.04597701 0.075 ] mean value: 0.07463915745296934 MCC on Blind test: 0.0 MCC on Training: 0.09 Running classifier: 15 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.01172972 0.00992846 0.00992441 0.00993395 0.01010275 0.00998449 0.00992608 0.00994134 0.01037979 0.01010704] mean value: 0.01019580364227295 key: score_time value: [0.01156497 0.00873709 0.00879812 0.00876832 0.00875545 0.00873494 0.00883245 0.00882673 0.00893879 0.0087831 ] mean value: 0.009073996543884277 key: test_mcc value: [-0.08099239 0.17998308 -0.11664237 0.17998308 -0.05399492 0.23179391 -0.07705141 -0.1110041 0.33071891 0.17890661] mean value: 0.06617004036883435 key: train_mcc value: [0.19384963 0.15589811 0.21883399 0.12222399 0.16592004 0.12318066 0.16592004 0.19959221 0.11427605 0.16802379] mean value: 0.16277185114134235 key: test_fscore value: [0. 0.18181818 0. 0.18181818 0. 0.30769231 0. 0. 0.22222222 0.18181818] mean value: 0.10753690753690752 key: train_fscore value: [0.22 0.18367347 0.22680412 0.16161616 0.1980198 0.18518519 0.1980198 0.22 0.15841584 0.2 ] mean value: 0.19517343854449964 key: test_precision value: [0. 0.5 0. 0.5 0. 0.4 0. 0. 1. 0.5] mean value: 0.29 key: train_precision value: [0.45833333 0.40909091 0.52380952 0.34782609 0.41666667 0.32258065 0.41666667 0.47826087 0.33333333 0.41666667] mean value: 0.4123234701250129 key: test_recall value: [0. 0.11111111 0. 0.11111111 0. 0.25 0. 0. 0.125 0.11111111] mean value: 0.07083333333333333 key: train_recall value: [0.14473684 0.11842105 0.14473684 0.10526316 0.12987013 0.12987013 0.12987013 0.14285714 0.1038961 0.13157895] mean value: 0.12811004784688992 key: test_accuracy value: [0.81034483 0.84482759 0.77586207 0.84482759 0.84210526 0.84210526 0.8245614 0.78947368 0.87719298 0.84210526] mean value: 0.8293405928614639 key: train_accuracy value: [0.84883721 0.84496124 0.85465116 0.83914729 0.84332689 0.82978723 0.84332689 0.84912959 0.83558994 0.84526112] mean value: 0.8434018562667746 key: test_roc_auc value: [0.47959184 0.54535147 0.45918367 0.54535147 0.48979592 0.59438776 0.47959184 0.45918367 0.5625 0.54513889] mean value: 0.5160076530612245 key: train_roc_auc value: [0.55759569 0.5444378 0.56100478 0.53558612 0.54902597 0.54107143 0.54902597 0.55779221 0.53376623 0.54991646] mean value: 0.5479222677906889 key: test_jcc value: [0. 0.1 0. 0.1 0. 0.18181818 0. 0. 0.125 0.1 ] mean value: 0.060681818181818184 key: train_jcc value: [0.12359551 0.1011236 0.12790698 0.08791209 0.10989011 0.10204082 0.10989011 0.12359551 0.08602151 0.11111111] mean value: 0.10830873239920527 MCC on Blind test: 0.15 MCC on Training: 0.07 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=12, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.02110171 0.01649809 0.01934862 0.02055526 0.02129412 0.01654077 0.02001953 0.01925993 0.0167551 0.01831222] mean value: 0.018968534469604493 key: score_time value: [0.01089811 0.01182866 0.01200104 0.01187968 0.01202941 0.01194644 0.01184773 0.01206493 0.01184821 0.01186943] mean value: 0.011821365356445313 key: test_mcc value: [ 0.37735271 -0.02129589 0. 0.30905755 -0.07705141 -0.05358409 0.08672195 0.00269975 0.15178571 -0.05786376] mean value: 0.07178225308222638 key: train_mcc value: [0.36933336 0.20448001 0.18400155 0.41862963 0.38084532 0.32010089 0.34135513 0.47602751 0.30853109 0.27886344] mean value: 0.3282167943776705 key: test_fscore value: [0.42857143 0.24489796 0. 0.2 0. 0.11111111 0.16666667 0.13333333 0.3030303 0. ] mean value: 0.1587610801896516 key: train_fscore value: [0.42016807 0.32054176 0.07594937 0.43636364 0.30434783 0.42682927 0.34615385 0.54014599 0.40247678 0.18823529] mean value: 0.3461211831639834 key: test_precision value: [0.6 0.15 0. 1. 0. 0.1 0.25 0.14285714 0.2 0. ] mean value: 0.2442857142857143 key: train_precision value: [0.58139535 0.19346049 1. 0.70588235 0.93333333 0.40229885 0.66666667 0.61666667 0.26422764 0.88888889] mean value: 0.6252820240648292 key: test_recall value: [0.33333333 0.66666667 0. 0.11111111 0. 0.125 0.125 0.125 0.625 0. ] mean value: 0.2111111111111111 key: train_recall value: [0.32894737 0.93421053 0.03947368 0.31578947 0.18181818 0.45454545 0.23376623 0.48051948 0.84415584 0.10526316] mean value: 0.391848940533151 key: test_accuracy value: [0.86206897 0.36206897 0.84482759 0.86206897 0.8245614 0.71929825 0.8245614 0.77192982 0.59649123 0.8245614 ] mean value: 0.7492437991530551 key: train_accuracy value: [0.86627907 0.41666667 0.85852713 0.87984496 0.8762089 0.81818182 0.86847195 0.87814313 0.62669246 0.86653772] mean value: 0.7955553806246533 key: test_roc_auc value: [0.6462585 0.48639456 0.5 0.55555556 0.47959184 0.47066327 0.53188776 0.50127551 0.60841837 0.48958333] mean value: 0.5269628684807256 key: train_roc_auc value: [0.64401914 0.63074163 0.51973684 0.6465311 0.58977273 0.66818182 0.60665584 0.71412338 0.7163961 0.55149779] mean value: 0.6287656370363137 key: test_jcc value: [0.27272727 0.13953488 0. 0.11111111 0. 0.05882353 0.09090909 0.07142857 0.17857143 0. ] mean value: 0.09231058878801698 key: train_jcc value: [0.26595745 0.19086022 0.03947368 0.27906977 0.17948718 0.27131783 0.20930233 0.37 0.25193798 0.1038961 ] mean value: 0.2161302536432828 MCC on Blind test: 0.15 MCC on Training: 0.07 Running classifier: 17 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.03031635 0.03039575 0.0305953 0.0300529 0.0295279 0.02967262 0.02949834 0.02915859 0.02946401 0.02937365] mean value: 0.029805541038513184 key: score_time value: [0.01271582 0.01270628 0.01266766 0.01282334 0.01255441 0.01265121 0.01308846 0.01289153 0.01263833 0.01259351] mean value: 0.012733054161071778 key: test_mcc value: [ 0.16714566 0.07128145 0.01078359 -0.08099239 -0.1110041 0.13095238 -0.07705141 -0.125294 -0.05399492 -0.08257228] mean value: -0.01507460316232791 key: train_mcc value: [0.21267408 0.26098171 0.23800939 0.23800939 0.25902767 0.21108253 0.25902767 0.28005602 0.2362278 0.26102234] mean value: 0.24561185832234034 key: test_fscore value: [0.26666667 0.15384615 0.13333333 0. 0. 0.18181818 0. 0. 0. 0. ] mean value: 0.07356643356643355 key: train_fscore value: [0.1 0.14634146 0.12345679 0.12345679 0.14457831 0.09876543 0.14457831 0.16666667 0.12195122 0.14634146] mean value: 0.1316136451859833 key: test_precision value: [0.33333333 0.25 0.16666667 0. 0. 0.33333333 0. 0. 0. 0. ] mean value: 0.10833333333333332 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.22222222 0.11111111 0.11111111 0. 0. 0.125 0. 0. 0. 0. ] mean value: 0.05694444444444444 key: train_recall value: [0.05263158 0.07894737 0.06578947 0.06578947 0.07792208 0.05194805 0.07792208 0.09090909 0.06493506 0.07894737] mean value: 0.07057416267942583 key: test_accuracy value: [0.81034483 0.81034483 0.77586207 0.81034483 0.78947368 0.84210526 0.8245614 0.77192982 0.84210526 0.80701754] mean value: 0.8084089534180279 key: train_accuracy value: [0.86046512 0.86434109 0.8624031 0.8624031 0.86266925 0.85880077 0.86266925 0.86460348 0.86073501 0.86460348] mean value: 0.8623693641011801 key: test_roc_auc value: [0.57029478 0.52494331 0.50453515 0.47959184 0.45918367 0.54209184 0.47959184 0.44897959 0.48979592 0.47916667] mean value: 0.49781746031746027 key: train_roc_auc value: [0.52631579 0.53947368 0.53289474 0.53289474 0.53896104 0.52597403 0.53896104 0.54545455 0.53246753 0.53947368] mean value: 0.535287081339713 key: test_jcc value: [0.15384615 0.08333333 0.07142857 0. 0. 0.1 0. 0. 0. 0. ] mean value: 0.04086080586080586 key: train_jcc value: [0.05263158 0.07894737 0.06578947 0.06578947 0.07792208 0.05194805 0.07792208 0.09090909 0.06493506 0.07894737] mean value: 0.07057416267942583 MCC on Blind test: -0.02 MCC on Training: -0.02 Running classifier: 18 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42))]) key: fit_time value: [0.77536511 0.70791936 0.69442844 0.70323062 0.71255398 0.72560024 0.70058107 0.71506214 0.75982857 0.71687603] mean value: 0.7211445569992065 key: score_time value: [0.1441853 0.16560936 0.22044325 0.20479393 0.16775441 0.17085862 0.18033099 0.17609906 0.17615891 0.18334889] mean value: 0.17895827293395997 key: test_mcc value: [ 0. 0.25920526 0.17998308 0. -0.05399492 0.4719399 0.19744425 0. 0.33071891 0.44038551] mean value: 0.1825681990549873 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0. 0.30769231 0.18181818 0. 0. 0.4 0.2 0. 0.22222222 0.36363636] mean value: 0.16753690753690756 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0. 0.5 0.5 0. 0. 1. 0.5 0. 1. 1. ] mean value: 0.45 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0. 0.22222222 0.11111111 0. 0. 0.25 0.125 0. 0.125 0.22222222] mean value: 0.10555555555555554 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.84482759 0.84482759 0.84482759 0.84482759 0.84210526 0.89473684 0.85964912 0.85964912 0.87719298 0.87719298] mean value: 0.858983666061706 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.5 0.59070295 0.54535147 0.5 0.48979592 0.625 0.55229592 0.5 0.5625 0.61111111] mean value: 0.5476757369614512 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0. 0.18181818 0.1 0. 0. 0.25 0.11111111 0. 0.125 0.22222222] mean value: 0.09901515151515151 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.26 MCC on Training: 0.18 Running classifier: 19 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea...age_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42))]) key: fit_time value: [1.0480988 1.02515268 1.03409672 1.06239414 1.05769014 1.06141448 1.04529428 1.02700305 1.08999658 1.07599425] mean value: 1.0527135133743286 key: score_time value: [0.26074052 0.16371894 0.24368358 0.24475455 0.27828455 0.25089765 0.24075389 0.28157997 0.26993084 0.22924829] mean value: 0.2463592767715454 key: test_mcc value: [ 0. 0.32993527 0. 0. -0.05399492 0. 0.33071891 0. 0. 0.3086067 ] mean value: 0.09152659559029659 key: train_mcc value: [0.53142577 0.57635312 0.5429362 0.51970872 0.57201256 0.5158001 0.57201256 0.59330379 0.59330379 0.51977207] mean value: 0.553662866849371 key: test_fscore value: [0. 0.33333333 0. 0. 0. 0. 0.22222222 0. 0. 0.2 ] mean value: 0.07555555555555556 key: train_fscore value: [0.48 0.53846154 0.4950495 0.46464646 0.53333333 0.46 0.53333333 0.56074766 0.56074766 0.46464646] mean value: 0.5090965966474432 key: test_precision value: [0. 0.66666667 0. 0. 0. 0. 1. 0. 0. 1. ] mean value: 0.26666666666666666 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0. 0.22222222 0. 0. 0. 0. 0.125 0. 0. 0.11111111] mean value: 0.04583333333333333 key: train_recall value: [0.31578947 0.36842105 0.32894737 0.30263158 0.36363636 0.2987013 0.36363636 0.38961039 0.38961039 0.30263158] mean value: 0.3423615857826384 key: test_accuracy value: [0.84482759 0.86206897 0.84482759 0.84482759 0.84210526 0.85964912 0.87719298 0.85964912 0.85964912 0.85964912] mean value: 0.8554446460980035 key: train_accuracy value: [0.89922481 0.90697674 0.90116279 0.89728682 0.90522244 0.89555126 0.90522244 0.90909091 0.90909091 0.89748549] mean value: 0.9026314605730736 key: test_roc_auc value: [0.5 0.60090703 0.5 0.5 0.48979592 0.5 0.5625 0.5 0.5 0.55555556] mean value: 0.5208758503401361 key: train_roc_auc value: [0.65789474 0.68421053 0.66447368 0.65131579 0.68181818 0.64935065 0.68181818 0.69480519 0.69480519 0.65131579] mean value: 0.6711807928913192 key: test_jcc value: [0. 0.2 0. 0. 0. 0. 0.125 0. 0. 0.11111111] mean value: 0.043611111111111114 key: train_jcc value: [0.31578947 0.36842105 0.32894737 0.30263158 0.36363636 0.2987013 0.36363636 0.38961039 0.38961039 0.30263158] mean value: 0.3423615857826384 MCC on Blind test: 0.25 MCC on Training: 0.09 Running classifier: 20 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) [0.03539228 0.02814484 0.03935695 0.0360446 0.0297637 0.03905463 0.03494239 0.0384376 0.03877997 0.03698874] mean value: 0.03569056987762451 key: score_time value: [0.04193258 0.01905274 0.02798605 0.03407478 0.01874733 0.01881433 0.01911497 0.02169275 0.01915193 0.01545525] mean value: 0.02360227108001709 key: test_mcc value: [-0.08099239 0.25920526 -0.08099239 0.32993527 -0.0952381 0.58333333 0.19744425 -0.07705141 0. 0.1134023 ] mean value: 0.11490461300356687 key: train_mcc value: [0.42925551 0.45352358 0.39012434 0.41237975 0.50035286 0.42413997 0.46287273 0.43903361 0.39831798 0.39824963] mean value: 0.4308249968705017 key: test_fscore value: [0. 0.30769231 0. 0.33333333 0. 0.54545455 0.2 0. 0. 0.16666667] mean value: 0.1553146853146853 key: train_fscore value: [0.41176471 0.42 0.37623762 0.35789474 0.48598131 0.3960396 0.43137255 0.42307692 0.37623762 0.35416667] mean value: 0.4032771741384019 key: test_precision value: [0. 0.5 0. 0.66666667 0. 1. 0.5 0. 0. 0.33333333] mean value: 0.3 key: train_precision value: [0.80769231 0.875 0.76 0.89473684 0.86666667 0.83333333 0.88 0.81481481 0.79166667 0.85 ] mean value: 0.8373910631279052 key: test_recall value: [0. 0.22222222 0. 0.22222222 0. 0.375 0.125 0. 0. 0.11111111] mean value: 0.10555555555555556 key: train_recall value: [0.27631579 0.27631579 0.25 0.22368421 0.33766234 0.25974026 0.28571429 0.28571429 0.24675325 0.22368421] mean value: 0.26655844155844155 key: test_accuracy value: [0.81034483 0.84482759 0.81034483 0.86206897 0.80701754 0.9122807 0.85964912 0.8245614 0.85964912 0.8245614 ] mean value: 0.8415305505142167 key: train_accuracy value: [0.88372093 0.8875969 0.87790698 0.88178295 0.89361702 0.88201161 0.88781431 0.88394584 0.87814313 0.88007737] mean value: 0.8836617036270674 key: test_roc_auc value: [0.47959184 0.59070295 0.47959184 0.60090703 0.46938776 0.6875 0.55229592 0.47959184 0.5 0.53472222] mean value: 0.5374291383219953 key: train_roc_auc value: [0.63247608 0.6347488 0.61818182 0.60956938 0.66428571 0.62532468 0.63944805 0.63717532 0.61769481 0.60844074] mean value: 0.6287345392702536 key: test_jcc value: [0. 0.18181818 0. 0.2 0. 0.375 0.11111111 0. 0. 0.09090909] mean value: 0.09588383838383839 key: train_jcc value: [0.25925926 0.26582278 0.23170732 0.21794872 0.32098765 0.24691358 0.275 0.26829268 0.23170732 0.21518987] mean value: 0.2532829187076897 MCC on Blind test: 0.23 MCC on Training: 0.11 Running classifier: 21 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=3))]) key: fit_time value: [0.13909769 0.16479492 0.10681844 0.11209536 0.12532425 0.14800119 0.12281156 0.10472059 0.12471604 0.09759688] mean value: 0.12459769248962402 key: score_time value: [0.02729654 0.01962543 0.01884365 0.01966977 0.01964426 0.01964855 0.01983571 0.0197382 0.019696 0.01992488] mean value: 0.020392298698425293 key: test_mcc value: [-0.08099239 -0.05676567 -0.05676567 0.30905755 -0.0952381 0. 0.19744425 -0.0952381 0. 0. ] mean value: 0.012150187297662157 key: train_mcc value: [0.25768548 0.18218537 0.21342259 0.21267408 0.50035286 0.15757516 0.46287273 0.52463889 0.39831798 0.15902041] mean value: 0.3068745554977592 key: test_fscore value: [0. 0. 0. 0.2 0. 0. 0.2 0. 0. 0. ] mean value: 0.04 key: train_fscore value: [0.16666667 0.09876543 0.14285714 0.1 0.48598131 0.09638554 0.43137255 0.52252252 0.37623762 0.09756098] mean value: 0.25183497631167273 key: test_precision value: [0. 0. 0. 1. 0. 0. 0.5 0. 0. 0. ] mean value: 0.15 key: train_precision value: [0.875 0.8 0.75 1. 0.86666667 0.66666667 0.88 0.85294118 0.79166667 0.66666667] mean value: 0.8149607843137255 key: test_recall value: [0. 0. 0. 0.11111111 0. 0. 0.125 0. 0. 0. ] mean value: 0.02361111111111111 key: train_recall value: [0.09210526 0.05263158 0.07894737 0.05263158 0.33766234 0.05194805 0.28571429 0.37662338 0.24675325 0.05263158] mean value: 0.1627648667122351 key: test_accuracy value: [0.81034483 0.82758621 0.82758621 0.86206897 0.80701754 0.85964912 0.85964912 0.80701754 0.85964912 0.84210526] mean value: 0.8362673926194798 key: train_accuracy value: [0.86434109 0.85852713 0.86046512 0.86046512 0.89361702 0.8549323 0.88781431 0.89748549 0.87814313 0.85686654] mean value: 0.8712657250386098 key: test_roc_auc value: [0.47959184 0.48979592 0.48979592 0.55555556 0.46938776 0.5 0.55229592 0.46938776 0.5 0.5 ] mean value: 0.5005810657596371 key: train_roc_auc value: [0.54491627 0.52517943 0.53720096 0.52631579 0.66428571 0.5237013 0.63944805 0.68262987 0.61769481 0.52404822] mean value: 0.5785420396228667 key: test_jcc value: [0. 0. 0. 0.11111111 0. 0. 0.11111111 0. 0. 0. ] mean value: 0.02222222222222222 key: train_jcc value: [0.09090909 0.05194805 0.07692308 0.05263158 0.32098765 0.05063291 0.275 0.35365854 0.23170732 0.05128205] mean value: 0.15556802693815688 MCC on Blind test: 0.03 MCC on Training: 0.01 Running classifier: 22 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.04572177 0.01867509 0.02096009 0.01873994 0.0185163 0.01975799 0.01879716 0.02220273 0.02242422 0.02311897] mean value: 0.022891426086425783 key: score_time value: [0.01312637 0.01114154 0.0119679 0.01122904 0.01119804 0.01180553 0.01122832 0.01227927 0.01128507 0.01178932] mean value: 0.01170504093170166 key: test_mcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_mcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_fscore value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_fscore value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_precision value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_precision value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_recall value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_recall value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_accuracy value: [0.84482759 0.84482759 0.84482759 0.84482759 0.85964912 0.85964912 0.85964912 0.85964912 0.85964912 0.84210526] mean value: 0.851966122202057 key: train_accuracy value: [0.85271318 0.85271318 0.85271318 0.85271318 0.85106383 0.85106383 0.85106383 0.85106383 0.85106383 0.85299807] mean value: 0.8519169927878488 key: test_roc_auc value: [0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5] mean value: 0.5 key: train_roc_auc value: [0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5] mean value: 0.5 key: test_jcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_jcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 MCC on Blind test: 0.0 MCC on Training: 0.0 Running classifier: 23 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.0122385 0.02054858 0.02327061 0.02312708 0.02878118 0.02456212 0.02054667 0.0241375 0.01982903 0.02272105] mean value: 0.021976232528686523 key: score_time value: [0.00973463 0.01091981 0.01166987 0.01163673 0.01174402 0.01169133 0.01174855 0.01166654 0.01238656 0.01189065] mean value: 0.011508870124816894 key: test_mcc value: [ 0.25920526 0.25920526 0.34240363 0.35172151 -0.0952381 0.35514145 0.05399492 -0.1400114 0. 0.25774177] mean value: 0.16441643112003362 key: train_mcc value: [0.40737686 0.51856909 0.51624668 0.42686641 0.5149146 0.48999792 0.13382992 0.51904882 0. 0.40168029] mean value: 0.39285305987684505 key: test_fscore value: [0.30769231 0.30769231 0.44444444 0.46153846 0. 0.42857143 0.25 0.08333333 0. 0.30769231] mean value: 0.25909645909645906 key: train_fscore value: [0.40384615 0.52252252 0.54237288 0.51141553 0.54545455 0.54545455 0.28205128 0.59 0. 0.38 ] mean value: 0.4323117455799137 key: test_precision value: [0.5 0.5 0.44444444 0.35294118 0. 0.5 0.14285714 0.0625 0. 0.5 ] mean value: 0.3002742763772176 key: train_precision value: [0.75 0.82857143 0.76190476 0.39160839 0.75 0.65454545 0.1641791 0.4796748 0. 0.79166667] mean value: 0.5572150604522282 key: test_recall value: [0.22222222 0.22222222 0.44444444 0.66666667 0. 0.375 1. 0.125 0. 0.22222222] mean value: 0.3277777777777778 key: train_recall value: [0.27631579 0.38157895 0.42105263 0.73684211 0.42857143 0.46753247 1. 0.76623377 0. 0.25 ] mean value: 0.47281271360218735 key: test_accuracy value: [0.84482759 0.84482759 0.82758621 0.75862069 0.80701754 0.85964912 0.15789474 0.61403509 0.85964912 0.84210526] mean value: 0.74162129461585 key: train_accuracy value: [0.87984496 0.89728682 0.89534884 0.79263566 0.89361702 0.88394584 0.2417795 0.84139265 0.85106383 0.88007737] mean value: 0.8056992487967254 key: test_roc_auc value: [0.59070295 0.59070295 0.67120181 0.72108844 0.46938776 0.65688776 0.51020408 0.40943878 0.5 0.59027778] mean value: 0.5709892290249433 key: train_roc_auc value: [0.63020335 0.68397129 0.69916268 0.76955742 0.70178571 0.71217532 0.55454545 0.81038961 0.5 0.61933107] mean value: 0.6681121906497847 key: test_jcc value: [0.18181818 0.18181818 0.28571429 0.3 0. 0.27272727 0.14285714 0.04347826 0. 0.18181818] mean value: 0.15902315076228118 key: train_jcc value: [0.25301205 0.35365854 0.37209302 0.34355828 0.375 0.375 0.1641791 0.41843972 0. 0.2345679 ] mean value: 0.2889508612266777 MCC on Blind test: 0.23 MCC on Training: 0.16 Running classifier: 24 Model_name: XGBoost Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.1525681 0.11051559 0.10556579 0.13596582 0.11572504 0.11393285 0.12484956 0.23212695 0.23624992 0.11793661] mean value: 0.14454362392425538 key: score_time value: [0.01112127 0.01105809 0.01123047 0.01124573 0.01113367 0.01189876 0.01169467 0.01227736 0.01176453 0.01155829] mean value: 0.011498284339904786 key: test_mcc value: [0.44095855 0.32350772 0.32350772 0.44095855 0.19744425 0.4719399 0.13095238 0.19744425 0.4719399 0.47222222] mean value: 0.3470875453265293 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.36363636 0.4 0.4 0.36363636 0.2 0.4 0.18181818 0.2 0.4 0.55555556] mean value: 0.3464646464646465 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.5 0.5 1. 0.5 1. 0.33333333 0.5 1. 0.55555556] mean value: 0.6888888888888889 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.22222222 0.33333333 0.33333333 0.22222222 0.125 0.25 0.125 0.125 0.25 0.55555556] mean value: 0.2541666666666667 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:463: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_CV['source_data'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:490: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_BT['source_data'] = 'BT' [0.87931034 0.84482759 0.84482759 0.87931034 0.85964912 0.89473684 0.84210526 0.85964912 0.89473684 0.85964912] mean value: 0.8658802177858439 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.61111111 0.63605442 0.63605442 0.61111111 0.55229592 0.625 0.54209184 0.55229592 0.625 0.73611111] mean value: 0.6127125850340136 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.22222222 0.25 0.25 0.22222222 0.11111111 0.25 0.1 0.11111111 0.25 0.38461538] mean value: 0.21512820512820513 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.31 MCC on Training: 0.35 Extracting tts_split_name: 70_30 Total cols in each df: CV df: 8 metaDF: 17 Adding column: Model_name Total cols in bts df: BT_df: 8 First proceeding to rowbind CV and BT dfs: Final output should have: 25 columns Combinig 2 using pd.concat by row ~ rowbind Checking Dims of df to combine: Dim of CV: (24, 8) Dim of BT: (24, 8) 8 Number of Common columns: 8 These are: ['source_data', 'Precision', 'JCC', 'MCC', 'Recall', 'Accuracy', 'F1', 'ROC_AUC'] Concatenating dfs with different resampling methods [WF]: Split type: 70_30 No. of dfs combining: 2 PASS: 2 dfs successfully combined nrows in combined_df_wf: 48 ncols in combined_df_wf: 8 PASS: proceeding to merge metadata with CV and BT dfs Adding column: Model_name ========================================================= SUCCESS: Ran multiple classifiers ======================================================= ============================================================== Running several classification models (n): 24 List of models: ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('Gaussian NB', GaussianNB()) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('K-Nearest Neighbors', KNeighborsClassifier()) ('LDA', LinearDiscriminantAnalysis()) ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(cv=3, random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Multinomial', MultinomialNB()) ('Naive Bayes', BernoulliNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=12, random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Random Forest', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42)) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=3)) ('SVC', SVC(random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=12, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ================================================================ Running classifier: 1 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.4207387 0.41027308 0.4036901 0.40408993 0.40696669 0.40004683 0.40256429 0.40490913 0.41152501 0.40068007] mean value: 0.4065483808517456 key: score_time value: [0.01641512 0.01615167 0.01700234 0.01601815 0.0159266 0.01930451 0.0159297 0.01599622 0.02474761 0.01606941] mean value: 0.017356133460998534 key: test_mcc value: [0.69402209 0.79658219 0.67403108 0.87755102 0.67403108 0.79591837 0.73484692 0.65319726 0.69471171 0.6940665 ] mean value: 0.7288958229370168 key: train_mcc value: [0.86591133 0.86592922 0.87960225 0.87045679 0.87280843 0.85462492 0.86591133 0.8795659 0.85699929 0.88422431] mean value: 0.8696033752633175 key: test_fscore value: [0.84848485 0.9 0.84 0.93877551 0.83333333 0.89795918 0.86868687 0.82474227 0.85148515 0.85436893] mean value: 0.8657836092977526 key: train_fscore value: [0.93303065 0.93318233 0.94011299 0.93515358 0.93679458 0.92776524 0.93303065 0.93997735 0.92881356 0.94197952] mean value: 0.9349840455770775 key: test_precision value: [0.84 0.88235294 0.82352941 0.93877551 0.85106383 0.89795918 0.86 0.83333333 0.81132075 0.81481481] mean value: 0.855314977947109 key: train_precision value: [0.93197279 0.93002257 0.93483146 0.93621868 0.93049327 0.92152466 0.93197279 0.93679458 0.92567568 0.94305239] mean value: 0.9322558878172098 key: test_recall value: [0.85714286 0.91836735 0.85714286 0.93877551 0.81632653 0.89795918 0.87755102 0.81632653 0.89583333 0.89795918] mean value: 0.8773384353741495 key: train_recall value: [0.93409091 0.93636364 0.94545455 0.93409091 0.94318182 0.93409091 0.93409091 0.94318182 0.93197279 0.94090909] mean value: 0.9377427334570193 key: test_accuracy value: [0.84693878 0.89795918 0.83673469 0.93877551 0.83673469 0.89795918 0.86734694 0.82653061 0.84536082 0.84536082] mean value: 0.8639701241321269 key: train_accuracy value: [0.93295455 0.93295455 0.93977273 0.93522727 0.93636364 0.92727273 0.93295455 0.93977273 0.92849035 0.94211124] mean value: 0.9347874316376019 key: test_roc_auc value: [0.84693878 0.89795918 0.83673469 0.93877551 0.83673469 0.89795918 0.86734694 0.82653061 0.84587585 0.84481293] mean value: 0.8639668367346939 key: train_roc_auc value: [0.93295455 0.93295455 0.93977273 0.93522727 0.93636364 0.92727273 0.93295455 0.93977273 0.92848639 0.94210987] mean value: 0.9347868996083282 key: test_jcc value: [0.73684211 0.81818182 0.72413793 0.88461538 0.71428571 0.81481481 0.76785714 0.70175439 0.74137931 0.74576271] mean value: 0.7649631319226662 key: train_jcc value: [0.87446809 0.87473461 0.8869936 0.87820513 0.88110403 0.86526316 0.87446809 0.88675214 0.86708861 0.89032258] mean value: 0.877940002590534 MCC on Blind test: 0.32 MCC on Training: 0.73 Running classifier: 2 Model_name: Bagging Classifier Model func: BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3) Running model pipeline: [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.4s remaining: 0.8s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.5s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. 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Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.4s remaining: 0.9s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.5s remaining: 0.2s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.5s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.5s remaining: 0.9s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.5s remaining: 0.2s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.5s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.4s remaining: 0.9s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.5s remaining: 0.2s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.5s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.5s remaining: 1.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.5s remaining: 0.2s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.5s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.4s remaining: 0.9s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.5s remaining: 0.2s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.5s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.5s remaining: 0.9s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.5s remaining: 0.2s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.5s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.5s remaining: 0.9s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.5s remaining: 0.2s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.5s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.4s remaining: 0.9s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.5s remaining: 0.2s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.5s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 4.0s remaining: 8.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 4.2s remaining: 8.4s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 4.2s remaining: 8.4s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 4.2s remaining: 8.4s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 4.2s remaining: 8.5s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 4.3s remaining: 8.5s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 4.3s remaining: 8.5s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 4.3s remaining: 8.6s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 4.3s remaining: 1.4s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 4.3s remaining: 1.4s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 4.3s remaining: 8.7s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 4.3s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 4.4s remaining: 1.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 4.4s remaining: 1.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 4.4s remaining: 1.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 4.4s remaining: 1.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 4.4s remaining: 1.5s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 4.4s remaining: 8.9s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 4.4s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 4.4s remaining: 1.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 4.5s remaining: 1.5s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 4.5s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 4.5s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 4.5s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 4.5s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 4.5s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 4.5s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 4.5s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 4.5s remaining: 1.5s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 4.5s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. 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[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3))]) key: fit_time value: [0.64677882 0.75415182 0.71715736 0.73595953 0.72241092 0.73925829 0.70149231 0.73364043 0.69988298 0.74195504] mean value: 0.7192687511444091 key: score_time value: [0.07763505 0.06509876 0.08245301 0.07057691 0.0478363 0.08257151 0.04315209 0.06950617 0.05735207 0.05796814] mean value: 0.06541500091552735 key: test_mcc value: [0.85732141 0.69402209 0.79858365 0.8996469 0.88048967 0.91913329 0.83743255 0.81649658 0.89868102 0.83567544] mean value: 0.8437482611240675 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.92783505 0.84848485 0.90196078 0.95049505 0.94117647 0.95833333 0.92 0.90909091 0.94949495 0.92 ] mean value: 0.9226871396357345 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.9375 0.84 0.86792453 0.92307692 0.90566038 0.9787234 0.90196078 0.9 0.92156863 0.90196078] mean value: 0.9078375429071052 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.91836735 0.85714286 0.93877551 0.97959184 0.97959184 0.93877551 0.93877551 0.91836735 0.97916667 0.93877551] mean value: 0.9387329931972788 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.92857143 0.84693878 0.89795918 0.94897959 0.93877551 0.95918367 0.91836735 0.90816327 0.94845361 0.91752577] mean value: 0.9212918156953502 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.92857143 0.84693878 0.89795918 0.94897959 0.93877551 0.95918367 0.91836735 0.90816327 0.94876701 0.91730442] mean value: 0.9213010204081632 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.86538462 0.73684211 0.82142857 0.90566038 0.88888889 0.92 0.85185185 0.83333333 0.90384615 0.85185185] mean value: 0.8579087749206915 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.37 MCC on Training: 0.84 Running classifier: 3 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.07125449 0.06175256 0.07926655 0.0707705 0.07266235 0.06858516 0.07720709 0.08052802 0.06904268 0.06716251] mean value: 0.07182319164276123 key: score_time value: [0.00907254 0.00909495 0.01021862 0.00985456 0.00973415 0.01015592 0.01044226 0.00927901 0.00944471 0.00920582] mean value: 0.009650254249572754 key: test_mcc value: [0.75763064 0.73484692 0.67346939 0.77696778 0.65982888 0.87828292 0.67403108 0.75510204 0.69844104 0.67006803] mean value: 0.7278668726789899 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.87234043 0.86597938 0.83673469 0.89108911 0.83809524 0.94 0.84 0.87755102 0.85436893 0.83673469] mean value: 0.8652893494183443 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.91111111 0.875 0.83673469 0.86538462 0.78571429 0.92156863 0.82352941 0.87755102 0.8 0.83673469] mean value: 0.8533328459588964 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.83673469 0.85714286 0.83673469 0.91836735 0.89795918 0.95918367 0.85714286 0.87755102 0.91666667 0.83673469] mean value: 0.879421768707483 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.87755102 0.86734694 0.83673469 0.8877551 0.82653061 0.93877551 0.83673469 0.87755102 0.84536082 0.83505155] mean value: 0.8629391962970756 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.87755102 0.86734694 0.83673469 0.8877551 0.82653061 0.93877551 0.83673469 0.87755102 0.84608844 0.83503401] mean value: 0.8630102040816328 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.77358491 0.76363636 0.71929825 0.80357143 0.72131148 0.88679245 0.72413793 0.78181818 0.74576271 0.71929825] mean value: 0.7639211942053337 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.13 MCC on Training: 0.73 Running classifier: 4 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.01147199 0.01145816 0.01151729 0.01153708 0.012326 0.01176715 0.01322985 0.01286674 0.01144385 0.01152539] mean value: 0.011914348602294922 key: score_time value: [0.00944352 0.00903201 0.00905132 0.00890088 0.00974011 0.00929642 0.00974417 0.00979781 0.00910211 0.00916672] mean value: 0.009327507019042969 key: test_mcc value: [0.76082741 0.53339646 0.63265306 0.61237244 0.6951817 0.6951817 0.66436384 0.64125732 0.73612968 0.5704578 ] mean value: 0.6541821412960007 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.86956522 0.75268817 0.81632653 0.80412371 0.85148515 0.85148515 0.8411215 0.83018868 0.87128713 0.77419355] mean value: 0.8262464780088822 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.93023256 0.79545455 0.81632653 0.8125 0.82692308 0.82692308 0.77586207 0.77192982 0.83018868 0.81818182] mean value: 0.8204522179006499 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.81632653 0.71428571 0.81632653 0.79591837 0.87755102 0.87755102 0.91836735 0.89795918 0.91666667 0.73469388] mean value: 0.8365646258503402 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.87755102 0.76530612 0.81632653 0.80612245 0.84693878 0.84693878 0.82653061 0.81632653 0.86597938 0.78350515] mean value: 0.8251525352409006 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.87755102 0.76530612 0.81632653 0.80612245 0.84693878 0.84693878 0.82653061 0.81632653 0.8664966 0.78401361] mean value: 0.8252551020408163 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.76923077 0.60344828 0.68965517 0.67241379 0.74137931 0.74137931 0.72580645 0.70967742 0.77192982 0.63157895] mean value: 0.7056499274197301 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.12 MCC on Training: 0.65 Running classifier: 5 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.17451382 0.1731205 0.1726656 0.1727879 0.17251396 0.17359877 0.17101431 0.171381 0.17287159 0.17214918] mean value: 0.1726616621017456 key: score_time value: [0.01846743 0.01851892 0.0185039 0.01876283 0.01851487 0.0184834 0.01868749 0.01855254 0.01854897 0.01856804] mean value: 0.01856083869934082 key: test_mcc value: [0.93897107 0.87755102 0.91913329 0.87828292 0.85732141 0.87828292 0.87755102 0.87755102 0.9587585 0.85738682] mean value: 0.892079000107983 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.96907216 0.93877551 0.96 0.94 0.92783505 0.94 0.93877551 0.93877551 0.97916667 0.92631579] mean value: 0.9458716203247441 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.97916667 0.93877551 0.94117647 0.92156863 0.9375 0.92156863 0.93877551 0.93877551 0.97916667 0.95652174] mean value: 0.9452995328566208 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.95918367 0.93877551 0.97959184 0.95918367 0.91836735 0.95918367 0.93877551 0.93877551 0.97916667 0.89795918] mean value: 0.9468962585034013 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.96938776 0.93877551 0.95918367 0.93877551 0.92857143 0.93877551 0.93877551 0.93877551 0.97938144 0.92783505] mean value: 0.9458236903008628 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.96938776 0.93877551 0.95918367 0.93877551 0.92857143 0.93877551 0.93877551 0.93877551 0.97937925 0.92814626] mean value: 0.9458545918367347 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.94 0.88461538 0.92307692 0.88679245 0.86538462 0.88679245 0.88461538 0.88461538 0.95918367 0.8627451 ] mean value: 0.8977821369476674 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.19 MCC on Training: 0.89 Running classifier: 6 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [1.79340768 1.78062367 1.86549115 1.82087874 1.77771759 1.81214905 1.82204747 1.8168788 1.88860893 1.90568972] mean value: 1.8283492803573609 key: score_time value: [0.00931811 0.00930262 0.0098927 0.0093565 0.00982285 0.00972223 0.0096283 0.00971198 0.01047206 0.00925827] mean value: 0.009648561477661133 key: test_mcc value: [0.79658219 0.83673469 0.79658219 0.95918367 0.83673469 0.8996469 0.83953666 0.75510204 0.87627551 0.81585945] mean value: 0.8412237991720138 key: train_mcc value: [0.98865934 0.98415445 0.99090909 0.98865934 0.98865934 0.99546483 0.97956822 0.98409345 0.99092966 0.98865177] mean value: 0.9879749505369066 key: test_fscore value: [0.89583333 0.91836735 0.9 0.97959184 0.91836735 0.94736842 0.92156863 0.87755102 0.9375 0.91089109] mean value: 0.9207039021966263 key: train_fscore value: [0.99429875 0.992 0.99545455 0.99429875 0.99429875 0.9977221 0.98973774 0.99203641 0.99545455 0.99431172] mean value: 0.9939613288923452 key: test_precision value: [0.91489362 0.91836735 0.88235294 0.97959184 0.91836735 0.97826087 0.88679245 0.87755102 0.9375 0.88461538] mean value: 0.9178292816228947 key: train_precision value: [0.99771167 0.99770115 0.99545455 0.99771167 0.99771167 1. 0.99313501 0.99316629 0.9977221 0.99544419] mean value: 0.9965758291795019 key: test_recall value: [0.87755102 0.91836735 0.91836735 0.97959184 0.91836735 0.91836735 0.95918367 0.87755102 0.9375 0.93877551] mean value: 0.9243622448979592 key: train_recall value: [0.99090909 0.98636364 0.99545455 0.99090909 0.99090909 0.99545455 0.98636364 0.99090909 0.99319728 0.99318182] mean value: 0.991365182436611 key: test_accuracy value: [0.89795918 0.91836735 0.89795918 0.97959184 0.91836735 0.94897959 0.91836735 0.87755102 0.93814433 0.90721649] mean value: 0.9202503681885126 key: train_accuracy value: [0.99431818 0.99204545 0.99545455 0.99431818 0.99431818 0.99772727 0.98977273 0.99204545 0.9954597 0.99432463] mean value: 0.9939784335981837 key: test_roc_auc value: [0.89795918 0.91836735 0.89795918 0.97959184 0.91836735 0.94897959 0.91836735 0.87755102 0.93813776 0.90688776] mean value: 0.9202168367346939 key: train_roc_auc value: [0.99431818 0.99204545 0.99545455 0.99431818 0.99431818 0.99772727 0.98977273 0.99204545 0.99546228 0.99432334] mean value: 0.9939785611214182 key: test_jcc value: [0.81132075 0.8490566 0.81818182 0.96 0.8490566 0.9 0.85454545 0.78181818 0.88235294 0.83636364] mean value: 0.8542695994349712 key: train_jcc value: [0.98866213 0.98412698 0.99095023 0.98866213 0.98866213 0.99545455 0.97968397 0.98419865 0.99095023 0.98868778] mean value: 0.9880038777943628 MCC on Blind test: 0.42 MCC on Training: 0.84 Running classifier: 7 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.01164412 0.01151538 0.01262808 0.01183867 0.01172996 0.01134276 0.01239514 0.0122993 0.01185489 0.01226401] mean value: 0.011951231956481933 key: score_time value: [0.00913882 0.00996852 0.00993657 0.00992203 0.01006317 0.00994396 0.00962567 0.00957918 0.00945687 0.00909162] mean value: 0.00967264175415039 key: test_mcc value: [0.34956935 0.26891436 0.41030497 0.57250257 0.18762219 0.57250257 0.4420318 0.47947893 0.39517786 0.27834703] mean value: 0.39564516417100193 key: train_mcc value: [0.4657247 0.48279961 0.44350241 0.44976387 0.47600548 0.43558217 0.49550352 0.48284641 0.43700732 0.47852444] mean value: 0.46472599116744207 key: test_fscore value: [0.69230769 0.66037736 0.7184466 0.79207921 0.62962963 0.79207921 0.74545455 0.75925926 0.72222222 0.65346535] mean value: 0.71653210716819 key: train_fscore value: [0.75052411 0.75402793 0.73862434 0.7434555 0.75395987 0.7366212 0.76390346 0.75912409 0.73572939 0.75653083] mean value: 0.7492500704216954 key: test_precision value: [0.65454545 0.61403509 0.68518519 0.76923077 0.57627119 0.76923077 0.67213115 0.69491525 0.65 0.63461538] mean value: 0.6720160238745813 key: train_precision value: [0.69649805 0.71486762 0.69108911 0.68932039 0.70414201 0.68421053 0.70955166 0.70134875 0.68910891 0.70019342] mean value: 0.6980330445993534 key: test_recall value: [0.73469388 0.71428571 0.75510204 0.81632653 0.69387755 0.81632653 0.83673469 0.83673469 0.8125 0.67346939] mean value: 0.7690051020408163 key: train_recall value: [0.81363636 0.79772727 0.79318182 0.80681818 0.81136364 0.79772727 0.82727273 0.82727273 0.78911565 0.82272727] mean value: 0.8086842918985775 key: test_accuracy value: [0.67346939 0.63265306 0.70408163 0.78571429 0.59183673 0.78571429 0.71428571 0.73469388 0.69072165 0.63917526] mean value: 0.6952345886808333 key: train_accuracy value: [0.72954545 0.73977273 0.71931818 0.72159091 0.73522727 0.71477273 0.74431818 0.7375 0.71623156 0.73552781] mean value: 0.7293804818904138 key: test_roc_auc value: [0.67346939 0.63265306 0.70408163 0.78571429 0.59183673 0.78571429 0.71428571 0.73469388 0.69196429 0.63881803] mean value: 0.6953231292517008 key: train_roc_auc value: [0.72954545 0.73977273 0.71931818 0.72159091 0.73522727 0.71477273 0.74431818 0.7375 0.71614873 0.73562667] mean value: 0.7293820861678004 key: test_jcc value: [0.52941176 0.49295775 0.56060606 0.6557377 0.45945946 0.6557377 0.5942029 0.6119403 0.56521739 0.48529412] mean value: 0.5610565147095936 key: train_jcc value: [0.60067114 0.60517241 0.58557047 0.59166667 0.60508475 0.58305648 0.6179966 0.61176471 0.5819398 0.60840336] mean value: 0.5991326386338309 MCC on Blind test: 0.08 MCC on Training: 0.4 Running classifier: 8 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.43400025 0.33842587 0.37816715 0.38791251 0.37124085 0.42798734 0.4531517 0.40805411 0.42444301 0.47730637] mean value: 0.4100689172744751 key: score_time value: [0.02094817 0.02114916 0.02104306 0.03905678 0.02172804 0.02097487 0.03689885 0.0209825 0.02078462 0.03615332] mean value: 0.02597193717956543 key: test_mcc value: [0.79858365 0.75510204 0.67403108 0.75510204 0.76082741 0.70588658 0.69751845 0.65982888 0.87707367 0.64965986] mean value: 0.7333613661711579 key: train_mcc value: [0.91142249 0.92742603 0.93182781 0.92051398 0.92505973 0.92505973 0.93190483 0.92057105 0.91606237 0.92296869] mean value: 0.9232816705633778 key: test_fscore value: [0.90196078 0.87755102 0.84 0.87755102 0.88461538 0.85981308 0.85436893 0.83809524 0.93877551 0.82474227] mean value: 0.8697473242236977 key: train_fscore value: [0.9559322 0.96396396 0.96598639 0.96045198 0.96271186 0.96271186 0.96613995 0.96054115 0.95828636 0.96171171] mean value: 0.9618437443146759 key: test_precision value: [0.86792453 0.87755102 0.82352941 0.87755102 0.83636364 0.79310345 0.81481481 0.78571429 0.92 0.83333333] mean value: 0.8429885499384853 key: train_precision value: [0.9505618 0.95535714 0.9638009 0.95505618 0.95730337 0.95730337 0.95964126 0.95302013 0.9529148 0.953125 ] mean value: 0.9558083954975489 key: test_recall value: [0.93877551 0.87755102 0.85714286 0.87755102 0.93877551 0.93877551 0.89795918 0.89795918 0.95833333 0.81632653] mean value: 0.8999149659863945 key: train_recall value: [0.96136364 0.97272727 0.96818182 0.96590909 0.96818182 0.96818182 0.97272727 0.96818182 0.96371882 0.97045455] mean value: 0.967962791177077 key: test_accuracy value: [0.89795918 0.87755102 0.83673469 0.87755102 0.87755102 0.84693878 0.84693878 0.82653061 0.93814433 0.82474227] mean value: 0.8650641699978963 key: train_accuracy value: [0.95568182 0.96363636 0.96590909 0.96022727 0.9625 0.9625 0.96590909 0.96022727 0.95800227 0.96140749] mean value: 0.9616000670725416 key: test_roc_auc value: [0.89795918 0.87755102 0.83673469 0.87755102 0.87755102 0.84693878 0.84693878 0.82653061 0.93835034 0.82482993] mean value: 0.8650935374149661 key: train_roc_auc value: [0.95568182 0.96363636 0.96590909 0.96022727 0.9625 0.9625 0.96590909 0.96022727 0.95799577 0.96141775] mean value: 0.961600443207586 key: test_jcc value: [0.82142857 0.78181818 0.72413793 0.78181818 0.79310345 0.75409836 0.74576271 0.72131148 0.88461538 0.70175439] mean value: 0.7709848632885559 key: train_jcc value: [0.91558442 0.93043478 0.93421053 0.92391304 0.92810458 0.92810458 0.93449782 0.92407809 0.91991342 0.92624729] mean value: 0.926508853443081 MCC on Blind test: 0.18 MCC on Training: 0.73 Running classifier: 9 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.01925564 0.01243234 0.01100016 0.01173997 0.01126504 0.01192713 0.01163507 0.01205778 0.01115322 0.01136589] mean value: 0.012383222579956055 key: score_time value: [0.0309186 0.01702595 0.01896214 0.02196646 0.02219987 0.02129841 0.01765037 0.02007532 0.01793265 0.01696014] mean value: 0.020498991012573242 key: test_mcc value: [0.74230749 0.73607474 0.65982888 0.69402209 0.78354679 0.6785758 0.58131836 0.52622833 0.81609262 0.54661603] mean value: 0.6764611134182366 key: train_mcc value: [0.80162477 0.78124417 0.79035489 0.80212413 0.77429665 0.79189958 0.80082773 0.80537789 0.78124923 0.79471036] mean value: 0.7923709397406419 key: test_fscore value: [0.87619048 0.87128713 0.83809524 0.84848485 0.8952381 0.84615385 0.80373832 0.78181818 0.90909091 0.78 ] mean value: 0.8450097041541476 key: train_fscore value: [0.9030837 0.89328933 0.89768977 0.90350877 0.88986784 0.89800443 0.90222222 0.90444444 0.89328933 0.89944751] mean value: 0.898484735569135 key: test_precision value: [0.82142857 0.84615385 0.78571429 0.84 0.83928571 0.8 0.74137931 0.70491803 0.88235294 0.76470588] mean value: 0.802593858424354 key: train_precision value: [0.87606838 0.86567164 0.86993603 0.87288136 0.86324786 0.87662338 0.8826087 0.88478261 0.86752137 0.87526882] mean value: 0.8734610136851497 key: test_recall value: [0.93877551 0.89795918 0.89795918 0.85714286 0.95918367 0.89795918 0.87755102 0.87755102 0.9375 0.79591837] mean value: 0.89375 key: train_recall value: [0.93181818 0.92272727 0.92727273 0.93636364 0.91818182 0.92045455 0.92272727 0.925 0.92063492 0.925 ] mean value: 0.9250180375180376 key: test_accuracy value: [0.86734694 0.86734694 0.82653061 0.84693878 0.8877551 0.83673469 0.78571429 0.75510204 0.90721649 0.77319588] mean value: 0.8353881758889123 key: train_accuracy value: [0.9 0.88977273 0.89431818 0.9 0.88636364 0.89545455 0.9 0.90227273 0.88989784 0.89670829] mean value: 0.895478794758023 key: test_roc_auc value: [0.86734694 0.86734694 0.82653061 0.84693878 0.8877551 0.83673469 0.78571429 0.75510204 0.90752551 0.77295918] mean value: 0.8353954081632653 key: train_roc_auc value: [0.9 0.88977273 0.89431818 0.9 0.88636364 0.89545455 0.9 0.90227273 0.88986291 0.89674036] mean value: 0.8954785095856523 key: test_jcc value: [0.77966102 0.77192982 0.72131148 0.73684211 0.81034483 0.73333333 0.671875 0.64179104 0.83333333 0.63934426] mean value: 0.7339766223507624 key: train_jcc value: [0.82329317 0.80715706 0.81437126 0.824 0.8015873 0.81488934 0.82186235 0.82555781 0.80715706 0.81726908] mean value: 0.815714441690133 MCC on Blind test: 0.14 MCC on Training: 0.68 Running classifier: 10 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.05845952 0.06707811 0.05475855 0.10527992 0.06763053 0.07034564 0.15840435 0.07434034 0.0924437 0.07908893] mean value: 0.08278295993804932 key: score_time value: [0.01898527 0.01242256 0.01411915 0.02597666 0.01383352 0.02055836 0.03019595 0.01885629 0.02098346 0.0122714 ] mean value: 0.01882026195526123 key: test_mcc value: [0.69402209 0.63265306 0.71488145 0.77567175 0.79658219 0.67403108 0.75510204 0.53611096 0.85720655 0.62951941] mean value: 0.7065780592251746 key: train_mcc value: [0.84161363 0.8182579 0.82510442 0.8185963 0.82965045 0.83419648 0.82501918 0.83865586 0.82076498 0.84122494] mean value: 0.8293084135899511 key: test_fscore value: [0.84848485 0.81632653 0.86 0.88659794 0.9 0.84 0.87755102 0.78095238 0.92473118 0.8125 ] mean value: 0.8547143901397668 key: train_fscore value: [0.9187935 0.90846682 0.9117984 0.90762125 0.91408935 0.9163803 0.91220068 0.91904219 0.90971429 0.91972477] mean value: 0.9137831540841166 key: test_precision value: [0.84 0.81632653 0.84313725 0.89583333 0.88235294 0.82352941 0.87755102 0.73214286 0.95555556 0.82978723] mean value: 0.8496216138937844 key: train_precision value: [0.93838863 0.91474654 0.91916859 0.92253521 0.92147806 0.92378753 0.91533181 0.9221968 0.91705069 0.92824074] mean value: 0.9222924596881367 key: test_recall value: [0.85714286 0.81632653 0.87755102 0.87755102 0.91836735 0.85714286 0.87755102 0.83673469 0.89583333 0.79591837] mean value: 0.8610119047619047 key: train_recall value: [0.9 0.90227273 0.90454545 0.89318182 0.90681818 0.90909091 0.90909091 0.91590909 0.90249433 0.91136364] mean value: 0.9054767058338486 key: test_accuracy value: [0.84693878 0.81632653 0.85714286 0.8877551 0.89795918 0.83673469 0.87755102 0.76530612 0.92783505 0.81443299] mean value: 0.8527982326951399 key: train_accuracy value: [0.92045455 0.90909091 0.9125 0.90909091 0.91477273 0.91704545 0.9125 0.91931818 0.91032917 0.92054484] mean value: 0.914564673408317 key: test_roc_auc value: [0.84693878 0.81632653 0.85714286 0.8877551 0.89795918 0.83673469 0.87755102 0.76530612 0.9275085 0.81462585] mean value: 0.8527848639455782 key: train_roc_auc value: [0.92045455 0.90909091 0.9125 0.90909091 0.91477273 0.91704545 0.9125 0.91931818 0.91033807 0.92053443] mean value: 0.9145645227788085 key: test_jcc value: [0.73684211 0.68965517 0.75438596 0.7962963 0.81818182 0.72413793 0.78181818 0.640625 0.86 0.68421053] mean value: 0.7486152996235801 key: train_jcc value: [0.84978541 0.83228512 0.83789474 0.83086681 0.84177215 0.84566596 0.83857442 0.85021097 0.83438155 0.85138004] mean value: 0.8412817169095916 MCC on Blind test: 0.24 MCC on Training: 0.71 Running classifier: 11 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.04553509 0.04668188 0.04499817 0.04514599 0.04497552 0.04415894 0.04555202 0.04617524 0.04879856 0.04510045] mean value: 0.04571218490600586 key: score_time value: [0.01211476 0.01200533 0.01240015 0.01192117 0.01203084 0.01201916 0.01214695 0.01217723 0.0133369 0.01218176] mean value: 0.012233424186706542 key: test_mcc value: [0.71428571 0.71667764 0.76537164 0.75573182 0.65982888 0.61545745 0.5573704 0.53979562 0.75329654 0.4500206 ] mean value: 0.6527836304162273 key: train_mcc value: [0.71291377 0.72425224 0.72464623 0.71631274 0.72008871 0.74825025 0.71845379 0.75414679 0.71874457 0.75737954] mean value: 0.7295188627297122 key: test_fscore value: [0.85714286 0.8627451 0.88679245 0.88 0.83809524 0.81553398 0.79646018 0.78504673 0.87755102 0.74766355] mean value: 0.834703110446317 key: train_fscore value: [0.86028603 0.86702703 0.86622807 0.8627451 0.86403509 0.87799564 0.86368593 0.88035126 0.86398259 0.88209607] mean value: 0.8688432808643138 key: test_precision value: [0.85714286 0.83018868 0.8245614 0.8627451 0.78571429 0.77777778 0.703125 0.72413793 0.86 0.68965517] mean value: 0.7915048204876467 key: train_precision value: [0.8336887 0.82680412 0.83686441 0.82845188 0.83474576 0.84309623 0.83018868 0.85138004 0.83054393 0.8487395 ] mean value: 0.8364503260278859 key: test_recall value: [0.85714286 0.89795918 0.95918367 0.89795918 0.89795918 0.85714286 0.91836735 0.85714286 0.89583333 0.81632653] mean value: 0.8855017006802722 key: train_recall value: [0.88863636 0.91136364 0.89772727 0.9 0.89545455 0.91590909 0.9 0.91136364 0.90022676 0.91818182] mean value: 0.9038863121005978 key: test_accuracy value: [0.85714286 0.85714286 0.87755102 0.87755102 0.82653061 0.80612245 0.76530612 0.76530612 0.87628866 0.72164948] mean value: 0.8230591205554386 key: train_accuracy value: [0.85568182 0.86022727 0.86136364 0.85681818 0.85909091 0.87272727 0.85795455 0.87613636 0.85811578 0.87741203] mean value: 0.8635527809307606 key: test_roc_auc value: [0.85714286 0.85714286 0.87755102 0.87755102 0.82653061 0.80612245 0.76530612 0.76530612 0.8764881 0.72066327] mean value: 0.8229804421768707 key: train_roc_auc value: [0.85568182 0.86022727 0.86136364 0.85681818 0.85909091 0.87272727 0.85795455 0.87613636 0.85806792 0.87745826] mean value: 0.8635526180169038 key: test_jcc value: [0.75 0.75862069 0.79661017 0.78571429 0.72131148 0.68852459 0.66176471 0.64615385 0.78181818 0.59701493] mean value: 0.718753286966227 key: train_jcc value: [0.75482625 0.76526718 0.76402321 0.75862069 0.76061776 0.78252427 0.76007678 0.78627451 0.7605364 0.7890625 ] mean value: 0.7681829547051306 MCC on Blind test: 0.27 MCC on Training: 0.65 Running classifier: 12 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(cv=3, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(cv=3, random_state=42))]) key: fit_time value: [0.64654088 0.59174943 0.60056496 0.69330025 0.6439333 0.59023786 0.72894526 0.6191802 0.59516859 0.61051917] mean value: 0.632013988494873 key: score_time value: [0.01221895 0.0121634 0.01221967 0.01226568 0.01220608 0.01217127 0.0190227 0.01220417 0.0121727 0.01241899] mean value: 0.012906360626220702 key: test_mcc value: [0.71428571 0.65319726 0.77567175 0.77696778 0.79591837 0.77567175 0.71970161 0.65982888 0.79633518 0.65581352] mean value: 0.7323391819618281 key: train_mcc value: [0.86435994 0.86141926 0.85917079 0.85245107 0.843184 0.86147266 0.865965 0.88190018 0.85255053 0.88424194] mean value: 0.8626715376795827 key: test_fscore value: [0.85714286 0.82828283 0.88888889 0.88421053 0.89795918 0.88888889 0.86538462 0.83809524 0.89130435 0.83809524] mean value: 0.86782526125939 key: train_fscore value: [0.93039443 0.93028571 0.92906178 0.92537313 0.92150171 0.930126 0.93257143 0.94050343 0.92571429 0.94184721] mean value: 0.9307379127006612 key: test_precision value: [0.85714286 0.82 0.88 0.91304348 0.89795918 0.88 0.81818182 0.78571429 0.93181818 0.78571429] mean value: 0.8569574090505767 key: train_precision value: [0.95023697 0.93563218 0.93548387 0.9350348 0.92255125 0.93764434 0.93793103 0.94700461 0.93317972 0.94508009] mean value: 0.9379778876946594 key: test_recall value: [0.85714286 0.83673469 0.89795918 0.85714286 0.89795918 0.89795918 0.91836735 0.89795918 0.85416667 0.89795918] mean value: 0.8813350340136055 key: train_recall value: [0.91136364 0.925 0.92272727 0.91590909 0.92045455 0.92272727 0.92727273 0.93409091 0.91836735 0.93863636] mean value: 0.9236549165120594 key: test_accuracy value: [0.85714286 0.82653061 0.8877551 0.8877551 0.89795918 0.8877551 0.85714286 0.82653061 0.89690722 0.82474227] mean value: 0.8650220913107513 key: train_accuracy value: [0.93181818 0.93068182 0.92954545 0.92613636 0.92159091 0.93068182 0.93295455 0.94090909 0.9262202 0.94211124] mean value: 0.9312649623361884 key: test_roc_auc value: [0.85714286 0.82653061 0.8877551 0.8877551 0.89795918 0.8877551 0.85714286 0.82653061 0.89647109 0.82397959] mean value: 0.8649022108843539 key: train_roc_auc value: [0.93181818 0.93068182 0.92954545 0.92613636 0.92159091 0.93068182 0.93295455 0.94090909 0.92622913 0.9421073 ] mean value: 0.9312654607297466 key: test_jcc value: [0.75 0.70689655 0.8 0.79245283 0.81481481 0.8 0.76271186 0.72131148 0.80392157 0.72131148] mean value: 0.7673420580581534 key: train_jcc value: [0.86984816 0.86965812 0.86752137 0.86111111 0.85443038 0.86937901 0.87366167 0.88768898 0.86170213 0.89008621] mean value: 0.8705087138881822 MCC on Blind test: 0.15 MCC on Training: 0.73 Running classifier: 13 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [3.1442554 3.38253736 2.5139215 2.999089 4.16743517 4.27736592 2.46986556 2.85411739 2.02810645 3.88314962] mean value: 3.171984338760376 key: score_time value: [0.02539682 0.01283503 0.01332092 0.0124743 0.01855421 0.01350784 0.01362324 0.01378441 0.01251578 0.01287293] mean value: 0.014888548851013183 key: test_mcc value: [0.83953666 0.75763064 0.75573182 0.79658219 0.80195322 0.70588658 0.70588658 0.69751845 0.81585945 0.7120375 ] mean value: 0.7588623072752595 key: train_mcc value: [0.95924948 0.96822183 0.94569882 0.95455532 0.9774343 0.97065762 0.93259889 0.9593387 0.89499292 0.98411144] mean value: 0.9546859316213808 key: test_fscore value: [0.92156863 0.87234043 0.88 0.9 0.90384615 0.85981308 0.85981308 0.85436893 0.90322581 0.85416667] mean value: 0.8809142780210463 key: train_fscore value: [0.97972973 0.98401826 0.97303371 0.97722096 0.98873874 0.98537683 0.96659243 0.97931034 0.94502924 0.99203641] mean value: 0.9771086643006432 key: test_precision value: [0.88679245 0.91111111 0.8627451 0.88235294 0.85454545 0.79310345 0.79310345 0.81481481 0.93333333 0.87234043] mean value: 0.8604242527934229 key: train_precision value: [0.97098214 0.98853211 0.96222222 0.97945205 0.97991071 0.97550111 0.94759825 0.99069767 0.97584541 0.99316629] mean value: 0.976390798317477 key: test_recall value: [0.95918367 0.83673469 0.89795918 0.91836735 0.95918367 0.93877551 0.93877551 0.89795918 0.875 0.83673469] mean value: 0.9058673469387755 key: train_recall value: [0.98863636 0.97954545 0.98409091 0.975 0.99772727 0.99545455 0.98636364 0.96818182 0.91609977 0.99090909] mean value: 0.978200886415172 key: test_accuracy value: [0.91836735 0.87755102 0.87755102 0.89795918 0.89795918 0.84693878 0.84693878 0.84693878 0.90721649 0.8556701 ] mean value: 0.8773090679570797 key: train_accuracy value: [0.97954545 0.98409091 0.97272727 0.97727273 0.98863636 0.98522727 0.96590909 0.97954545 0.94665153 0.99205448] mean value: 0.9771660561345579 key: test_roc_auc value: [0.91836735 0.87755102 0.87755102 0.89795918 0.89795918 0.84693878 0.84693878 0.84693878 0.90688776 0.85586735] mean value: 0.877295918367347 key: train_roc_auc value: [0.97954545 0.98409091 0.97272727 0.97727273 0.98863636 0.98522727 0.96590909 0.97954545 0.94668625 0.99205318] mean value: 0.9771693980622553 key: test_jcc value: [0.85454545 0.77358491 0.78571429 0.81818182 0.8245614 0.75409836 0.75409836 0.74576271 0.82352941 0.74545455] mean value: 0.7879531258005841 key: train_jcc value: [0.9602649 0.96853933 0.94748359 0.95545657 0.97772829 0.97117517 0.93534483 0.95945946 0.89578714 0.98419865] mean value: 0.9555437908990803 MCC on Blind test: 0.25 MCC on Training: 0.76 Running classifier: 14 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.01519608 0.01554847 0.01526856 0.0153029 0.01531029 0.0152781 0.01523685 0.01532555 0.01585746 0.01545882] mean value: 0.015378308296203614 key: score_time value: [0.01229715 0.01218152 0.01214623 0.01227713 0.01240396 0.01217532 0.01227832 0.01222944 0.01221633 0.01231265] mean value: 0.012251806259155274 key: test_mcc value: [0.2655274 0.47294677 0.51062961 0.44982345 0.35165724 0.44982345 0.37370466 0.30612245 0.36096939 0.19565281] mean value: 0.37368572249634846 key: train_mcc value: [0.34805193 0.45037702 0.43468519 0.39650454 0.41839366 0.40877489 0.4456203 0.43246199 0.32907411 0.43264048] mean value: 0.4096584125136621 key: test_fscore value: [0.64 0.75 0.75 0.73267327 0.69811321 0.73267327 0.71028037 0.65306122 0.68041237 0.61386139] mean value: 0.6961075097794842 key: train_fscore value: [0.6807564 0.73051225 0.72425249 0.70833333 0.71364653 0.71722644 0.7264574 0.72345133 0.67755991 0.71973094] mean value: 0.712192701953605 key: test_precision value: [0.62745098 0.70909091 0.76595745 0.71153846 0.64912281 0.71153846 0.65517241 0.65306122 0.67346939 0.59615385] mean value: 0.675255593857789 key: train_precision value: [0.66666667 0.71615721 0.7062635 0.68432203 0.70264317 0.68530021 0.71681416 0.70474138 0.65199161 0.71017699] mean value: 0.6945076927579326 key: test_recall value: [0.65306122 0.79591837 0.73469388 0.75510204 0.75510204 0.75510204 0.7755102 0.65306122 0.6875 0.63265306] mean value: 0.7197704081632652 key: train_recall value: [0.69545455 0.74545455 0.74318182 0.73409091 0.725 0.75227273 0.73636364 0.74318182 0.70521542 0.72954545] mean value: 0.7309760874046588 key: test_accuracy value: [0.63265306 0.73469388 0.75510204 0.7244898 0.67346939 0.7244898 0.68367347 0.65306122 0.68041237 0.59793814] mean value: 0.6859983168525142 key: train_accuracy value: [0.67386364 0.725 0.71704545 0.69772727 0.70909091 0.70340909 0.72272727 0.71590909 0.66401816 0.71623156] mean value: 0.7045022443504283 key: test_roc_auc value: [0.63265306 0.73469388 0.75510204 0.7244898 0.67346939 0.7244898 0.68367347 0.65306122 0.68048469 0.59757653] mean value: 0.6859693877551021 key: train_roc_auc value: [0.67386364 0.725 0.71704545 0.69772727 0.70909091 0.70340909 0.72272727 0.71590909 0.66397135 0.71624665] mean value: 0.7044990723562151 key: test_jcc value: [0.47058824 0.6 0.6 0.578125 0.53623188 0.578125 0.55072464 0.48484848 0.515625 0.44285714] mean value: 0.5357125384738877 key: train_jcc value: [0.51602024 0.5754386 0.56770833 0.5483871 0.55478261 0.55912162 0.57042254 0.56672444 0.51235585 0.56217163] mean value: 0.5533132942113221 MCC on Blind test: 0.18 MCC on Training: 0.37 Running classifier: 15 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.01575112 0.01589584 0.01608348 0.01582384 0.015939 0.02762294 0.01662326 0.01590466 0.01635742 0.01611567] mean value: 0.017211723327636718 key: score_time value: [0.01246548 0.01248479 0.01250196 0.01290655 0.01290131 0.01252508 0.01262379 0.01261425 0.01249528 0.01223207] mean value: 0.012575054168701172 key: test_mcc value: [0.39167473 0.3985267 0.48413007 0.59381861 0.53611096 0.47577156 0.54451008 0.48413007 0.49401478 0.51516729] mean value: 0.49178548526535915 key: train_mcc value: [0.54124755 0.56402245 0.5448478 0.51754579 0.52250335 0.53210643 0.53187561 0.55395398 0.52825506 0.52848022] mean value: 0.5364838255245152 key: test_fscore value: [0.73043478 0.73504274 0.76363636 0.80392157 0.78095238 0.75471698 0.78899083 0.76363636 0.76190476 0.77777778] mean value: 0.7661014541006679 key: train_fscore value: [0.78681771 0.79796954 0.78800414 0.77709611 0.78014184 0.78367347 0.78419453 0.79393939 0.78153846 0.7826087 ] mean value: 0.7855983901233304 key: test_precision value: [0.63636364 0.63235294 0.68852459 0.77358491 0.73214286 0.70175439 0.71666667 0.68852459 0.70175439 0.71186441] mean value: 0.6983533366047363 key: train_precision value: [0.71939736 0.72110092 0.72296015 0.7063197 0.70383912 0.71111111 0.70749543 0.71454545 0.71348315 0.70491803] mean value: 0.7125170431914484 key: test_recall value: [0.85714286 0.87755102 0.85714286 0.83673469 0.83673469 0.81632653 0.87755102 0.85714286 0.83333333 0.85714286] mean value: 0.8506802721088433 key: train_recall value: [0.86818182 0.89318182 0.86590909 0.86363636 0.875 0.87272727 0.87954545 0.89318182 0.86394558 0.87954545] mean value: 0.8754854669140384 key: test_accuracy value: [0.68367347 0.68367347 0.73469388 0.79591837 0.76530612 0.73469388 0.76530612 0.73469388 0.74226804 0.75257732] mean value: 0.7392804544498212 key: train_accuracy value: [0.76477273 0.77386364 0.76704545 0.75227273 0.75340909 0.75909091 0.75795455 0.76818182 0.75822928 0.75595914] mean value: 0.7610779331338355 key: test_roc_auc value: [0.68367347 0.68367347 0.73469388 0.79591837 0.76530612 0.73469388 0.76530612 0.73469388 0.74319728 0.7514881 ] mean value: 0.7392644557823129 key: train_roc_auc value: [0.76477273 0.77386364 0.76704545 0.75227273 0.75340909 0.75909091 0.75795455 0.76818182 0.75810915 0.75609926] mean value: 0.761079931972789 key: test_jcc value: [0.57534247 0.58108108 0.61764706 0.67213115 0.640625 0.60606061 0.65151515 0.61764706 0.61538462 0.63636364] mean value: 0.6213797821346558 key: train_jcc value: [0.64855688 0.66385135 0.65017065 0.63545151 0.63953488 0.6442953 0.645 0.65829146 0.64141414 0.64285714] mean value: 0.6469423308185427 MCC on Blind test: 0.08 MCC on Training: 0.49 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.03209734 0.0274663 0.027637 0.03922153 0.0291934 0.03330278 0.03730226 0.04032254 0.02888703 0.03684497] mean value: 0.03322751522064209 key: score_time value: [0.01231146 0.01233363 0.0129137 0.0123167 0.01658416 0.01221919 0.01661897 0.02168465 0.01244879 0.01227212] mean value: 0.014170336723327636 key: test_mcc value: [0.71205164 0.49692935 0.53611096 0.75573182 0.27607882 0.64125732 0.46945693 0.50467244 0.29631801 0.53871976] mean value: 0.5227327042834328 key: train_mcc value: [0.67105279 0.5951397 0.58781536 0.73193917 0.3919309 0.67615215 0.71248209 0.71079188 0.30470931 0.72673788] mean value: 0.6108751232226804 key: test_fscore value: [0.86238532 0.77310924 0.74725275 0.875 0.4 0.8 0.76271186 0.77586207 0.33898305 0.78899083] mean value: 0.7124295121958971 key: train_fscore value: [0.84422111 0.81060606 0.74901445 0.8646789 0.43262411 0.82381531 0.86278586 0.86147624 0.32075472 0.86893705] mean value: 0.7438913810093881 key: test_precision value: [0.78333333 0.65714286 0.80952381 0.89361702 0.8125 0.87804878 0.65217391 0.67164179 0.90909091 0.71666667] mean value: 0.7783739081610231 key: train_precision value: [0.75675676 0.69480519 0.88785047 0.87268519 0.98387097 0.88511749 0.79501916 0.77595628 0.95505618 0.79584121] mean value: 0.8402958896097653 key: test_recall value: [0.95918367 0.93877551 0.69387755 0.85714286 0.26530612 0.73469388 0.91836735 0.91836735 0.20833333 0.87755102] mean value: 0.7371598639455782 key: train_recall value: [0.95454545 0.97272727 0.64772727 0.85681818 0.27727273 0.77045455 0.94318182 0.96818182 0.19274376 0.95681818] mean value: 0.7540471036899608 key: test_accuracy value: [0.84693878 0.7244898 0.76530612 0.87755102 0.60204082 0.81632653 0.71428571 0.73469388 0.59793814 0.7628866 ] mean value: 0.7442457395329265 key: train_accuracy value: [0.82386364 0.77272727 0.78295455 0.86590909 0.63636364 0.83522727 0.85 0.84431818 0.59137344 0.85584563] mean value: 0.7858582705603137 key: test_roc_auc value: [0.84693878 0.7244898 0.76530612 0.87755102 0.60204082 0.81632653 0.71428571 0.73469388 0.59396259 0.76169218] mean value: 0.7437287414965985 key: train_roc_auc value: [0.82386364 0.77272727 0.78295455 0.86590909 0.63636364 0.83522727 0.85 0.84431818 0.59182643 0.85596011] mean value: 0.7859150175221604 key: test_jcc value: [0.75806452 0.63013699 0.59649123 0.77777778 0.25 0.66666667 0.61643836 0.63380282 0.20408163 0.65151515] mean value: 0.5784975132179027 key: train_jcc value: [0.73043478 0.68152866 0.5987395 0.76161616 0.2760181 0.70041322 0.75868373 0.75666075 0.19101124 0.76824818] mean value: 0.6223354311705929 MCC on Blind test: 0.23 MCC on Training: 0.52 Running classifier: 17 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.03982592 0.03873658 0.03802419 0.03839374 0.04023647 0.03872871 0.04260731 0.03917837 0.03962421 0.0406208 ] mean value: 0.03959763050079346 key: score_time value: [0.01279473 0.01305819 0.01302433 0.01301193 0.01299763 0.0130589 0.01294136 0.01387525 0.0137527 0.01377273] mean value: 0.013228774070739746 key: test_mcc value: [0.95998366 0.9797959 0.95998366 0.9797959 0.92144268 0.95998366 0.94053994 0.8660254 1. 0.82850613] mean value: 0.9396056930192345 key: train_mcc value: [0.93826536 0.95118973 0.94902474 0.95553309 0.96647087 0.95553309 0.95771151 0.96647087 0.94261574 0.96212582] mean value: 0.9544940795886984 key: test_fscore value: [0.98 0.98989899 0.98 0.98989899 0.96078431 0.98 0.97029703 0.93333333 1. 0.91588785] mean value: 0.9700100507027063 key: train_fscore value: [0.969163 0.97560976 0.97452935 0.97777778 0.98324022 0.97777778 0.97886541 0.98324022 0.97136564 0.98104794] mean value: 0.9772617083140446 key: test_precision value: [0.96078431 0.98 0.96078431 0.98 0.9245283 0.96078431 0.94230769 0.875 1. 0.84482759] mean value: 0.9429016521577853 key: train_precision value: [0.94017094 0.95238095 0.95032397 0.95652174 0.96703297 0.95652174 0.95860566 0.96703297 0.94432548 0.96280088] mean value: 0.9555717300521025 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.97959184 0.98979592 0.97959184 0.98979592 0.95918367 0.97959184 0.96938776 0.92857143 1. 0.90721649] mean value: 0.9682726698926993 key: train_accuracy value: [0.96818182 0.975 0.97386364 0.97727273 0.98295455 0.97727273 0.97840909 0.98295455 0.97048808 0.98070375] mean value: 0.9767100918377878 key: test_roc_auc value: [0.97959184 0.98979592 0.97959184 0.98979592 0.95918367 0.97959184 0.96938776 0.92857143 1. 0.90625 ] mean value: 0.9681760204081632 key: train_roc_auc value: [0.96818182 0.975 0.97386364 0.97727273 0.98295455 0.97727273 0.97840909 0.98295455 0.97045455 0.98072562] mean value: 0.9767089259946402 key: test_jcc value: [0.96078431 0.98 0.96078431 0.98 0.9245283 0.96078431 0.94230769 0.875 1. 0.84482759] mean value: 0.9429016521577853 key: train_jcc value: [0.94017094 0.95238095 0.95032397 0.95652174 0.96703297 0.95652174 0.95860566 0.96703297 0.94432548 0.96280088] mean value: 0.9555717300521025 MCC on Blind test: -0.01 MCC on Training: 0.94 Running classifier: 18 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42))]) key: fit_time value: [0.90203786 0.98160458 0.91370368 0.90036654 0.85720801 0.88877225 0.89762831 0.91018414 0.9318676 0.87975812] mean value: 0.9063131093978882 key: score_time value: [0.14284611 0.1596992 0.17170238 0.15744877 0.18316221 0.17896438 0.21035004 0.16290617 0.16349888 0.25874066] mean value: 0.1789318799972534 key: test_mcc value: [0.89814624 0.83743255 0.83743255 0.93897107 0.81649658 0.95918367 0.83673469 0.83953666 0.95959175 0.79455558] mean value: 0.8718081354056307 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.94845361 0.91666667 0.91666667 0.96969697 0.90721649 0.97959184 0.91836735 0.91489362 0.97959184 0.89583333] mean value: 0.9346978376885859 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.95833333 0.93617021 0.93617021 0.96 0.91666667 0.97959184 0.91836735 0.95555556 0.96 0.91489362] mean value: 0.9435748781782216 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.93877551 0.89795918 0.89795918 0.97959184 0.89795918 0.97959184 0.91836735 0.87755102 1. 0.87755102] mean value: 0.926530612244898 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.94897959 0.91836735 0.91836735 0.96938776 0.90816327 0.97959184 0.91836735 0.91836735 0.97938144 0.89690722] mean value: 0.9355880496528508 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.94897959 0.91836735 0.91836735 0.96938776 0.90816327 0.97959184 0.91836735 0.91836735 0.97959184 0.89710884] mean value: 0.9356292517006803 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.90196078 0.84615385 0.84615385 0.94117647 0.83018868 0.96 0.8490566 0.84313725 0.96 0.81132075] mean value: 0.8789148239847464 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.28 MCC on Training: 0.87 Running classifier: 19 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea...age_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42))]) key: fit_time value: [1.17923307 1.27183652 1.26172757 1.27168036 1.24040389 1.26755142 1.26056337 1.21210647 1.26038289 1.22063065] mean value: 1.2446116209030151 key: score_time value: [0.26458311 0.22890043 0.26221085 0.26417589 0.26643586 0.2054975 0.32459259 0.25713944 0.27757573 0.24479532] mean value: 0.25959067344665526 key: test_mcc value: [0.85875386 0.83743255 0.83743255 0.95998366 0.75573182 0.91836735 0.81649658 0.73484692 0.87696964 0.77479842] mean value: 0.8370813354495187 key: train_mcc value: [0.96843196 0.96379568 0.96603135 0.96148532 0.95479208 0.96178351 0.9593387 0.96597146 0.96160906 0.96376644] mean value: 0.9627005565626752 key: test_fscore value: [0.92631579 0.91666667 0.91666667 0.97916667 0.875 0.95918367 0.90721649 0.86868687 0.93617021 0.88421053] mean value: 0.9169283565557047 key: train_fscore value: [0.98390805 0.98165138 0.98281787 0.98052692 0.97701149 0.98039216 0.97931034 0.98285714 0.98052692 0.98169336] mean value: 0.9810695631526846 key: test_precision value: [0.95652174 0.93617021 0.93617021 1. 0.89361702 0.95918367 0.91666667 0.86 0.95652174 0.91304348] mean value: 0.9327894743466304 key: train_precision value: [0.99534884 0.99074074 0.99076212 0.98845266 0.98837209 0.99531616 0.99069767 0.98850575 0.99074074 0.98847926] mean value: 0.9907416035782941 key: test_recall value: [0.89795918 0.89795918 0.89795918 0.95918367 0.85714286 0.95918367 0.89795918 0.87755102 0.91666667 0.85714286] mean value: 0.9018707482993197 key: train_recall value: [0.97272727 0.97272727 0.975 0.97272727 0.96590909 0.96590909 0.96818182 0.97727273 0.97052154 0.975 ] mean value: 0.9715976087404659 key: test_accuracy value: [0.92857143 0.91836735 0.91836735 0.97959184 0.87755102 0.95918367 0.90816327 0.86734694 0.93814433 0.88659794] mean value: 0.9181885125184095 key: train_accuracy value: [0.98409091 0.98181818 0.98295455 0.98068182 0.97727273 0.98068182 0.97954545 0.98295455 0.98070375 0.98183882] mean value: 0.9812542565266742 key: test_roc_auc value: [0.92857143 0.91836735 0.91836735 0.97959184 0.87755102 0.95918367 0.90816327 0.86734694 0.93792517 0.88690476] mean value: 0.9181972789115648 key: train_roc_auc value: [0.98409091 0.98181818 0.98295455 0.98068182 0.97727273 0.98068182 0.97954545 0.98295455 0.98071532 0.98183107] mean value: 0.981254638218924 key: test_jcc value: [0.8627451 0.84615385 0.84615385 0.95918367 0.77777778 0.92156863 0.83018868 0.76785714 0.88 0.79245283] mean value: 0.848408152133616 key: train_jcc value: [0.96832579 0.96396396 0.96621622 0.96179775 0.95505618 0.96153846 0.95945946 0.96629213 0.96179775 0.96404494] mean value: 0.9628492657078249 MCC on Blind test: 0.39 MCC on Training: 0.84 Running classifier: 20 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.01617455 0.03735232 0.05062556 0.04543686 0.04260755 0.04300332 0.04999232 0.02893782 0.01796651 0.01834822] mean value: 0.035044503211975095 key: score_time value: [0.01238537 0.0201304 0.01942277 0.02481437 0.01950455 0.01937199 0.02686667 0.01454735 0.01260424 0.01244187] mean value: 0.018208956718444823 key: test_mcc value: [0.77567175 0.75510204 0.76537164 0.75763064 0.6785758 0.63318071 0.58131836 0.53979562 0.77338435 0.53058929] mean value: 0.6790620209736359 key: train_mcc value: [0.77069543 0.77213345 0.77807694 0.76004156 0.78498548 0.81050718 0.77933652 0.79707008 0.77908493 0.8040222 ] mean value: 0.7835953760569103 key: test_fscore value: [0.88888889 0.87755102 0.88679245 0.88235294 0.84615385 0.82 0.80373832 0.78504673 0.88659794 0.78095238] mean value: 0.8458074515283242 key: train_fscore value: [0.88664422 0.88888889 0.89111111 0.88248337 0.8945616 0.90728477 0.89254386 0.90088106 0.89230769 0.90407938] mean value: 0.8940785948507388 key: test_precision value: [0.88 0.87755102 0.8245614 0.8490566 0.8 0.80392157 0.74137931 0.72413793 0.87755102 0.73214286] mean value: 0.8110301715248301 key: train_precision value: [0.87583149 0.86140725 0.87173913 0.86147186 0.87418655 0.88197425 0.86228814 0.87393162 0.86567164 0.87794433] mean value: 0.8706446253662043 key: test_recall value: [0.89795918 0.87755102 0.95918367 0.91836735 0.89795918 0.83673469 0.87755102 0.85714286 0.89583333 0.83673469] mean value: 0.885501700680272 key: train_recall value: [0.89772727 0.91818182 0.91136364 0.90454545 0.91590909 0.93409091 0.925 0.92954545 0.92063492 0.93181818] mean value: 0.9188816738816739 key: test_accuracy value: [0.8877551 0.87755102 0.87755102 0.87755102 0.83673469 0.81632653 0.78571429 0.76530612 0.88659794 0.7628866 ] mean value: 0.8373974332000842 key: train_accuracy value: [0.88522727 0.88522727 0.88863636 0.87954545 0.89204545 0.90454545 0.88863636 0.89772727 0.88876277 0.90124858] mean value: 0.8911602259828706 key: test_roc_auc value: [0.8877551 0.87755102 0.87755102 0.87755102 0.83673469 0.81632653 0.78571429 0.76530612 0.88669218 0.76211735] mean value: 0.8373299319727892 key: train_roc_auc value: [0.88522727 0.88522727 0.88863636 0.87954545 0.89204545 0.90454545 0.88863636 0.89772727 0.88872655 0.90128324] mean value: 0.8911600700886415 key: test_jcc value: [0.8 0.78181818 0.79661017 0.78947368 0.73333333 0.69491525 0.671875 0.64615385 0.7962963 0.640625 ] mean value: 0.7351100765540997 key: train_jcc value: [0.79637097 0.8 0.80360721 0.78968254 0.80923695 0.83030303 0.80594059 0.81963928 0.80555556 0.8249497 ] mean value: 0.8085285826308738 MCC on Blind test: 0.26 MCC on Training: 0.68 Running classifier: 21 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=3))]) key: fit_time value: [0.13147521 0.15623641 0.13409472 0.14992619 0.15557718 0.20729351 0.1383872 0.15903759 0.13460326 0.15297318] mean value: 0.15196044445037843 key: score_time value: [0.02241683 0.01992941 0.01988912 0.01984739 0.01978898 0.02170968 0.01976275 0.01979566 0.01991224 0.0293138 ] mean value: 0.021236586570739745 key: test_mcc value: [0.6951817 0.6951817 0.73607474 0.67403108 0.79858365 0.65428866 0.6951817 0.57442696 0.79379252 0.62882653] mean value: 0.6945569223291919 key: train_mcc value: [0.82561646 0.82276127 0.82286329 0.8185963 0.82758056 0.83195571 0.82273577 0.82956474 0.81629246 0.83208346] mean value: 0.8250050019078303 key: test_fscore value: [0.84210526 0.84210526 0.87128713 0.84 0.90196078 0.83168317 0.85148515 0.7961165 0.89583333 0.81632653] mean value: 0.8488903124974015 key: train_fscore value: [0.91077636 0.9109589 0.91055046 0.90762125 0.9124424 0.91513761 0.91116173 0.91448119 0.90721649 0.91533181] mean value: 0.9115678202154021 key: test_precision value: [0.86956522 0.86956522 0.84615385 0.82352941 0.86792453 0.80769231 0.82692308 0.75925926 0.89583333 0.81632653] mean value: 0.8382772728823269 key: train_precision value: [0.92907801 0.91513761 0.91898148 0.92253521 0.92523364 0.92361111 0.91324201 0.91762014 0.91666667 0.92165899] mean value: 0.9203764876857281 key: test_recall value: [0.81632653 0.81632653 0.89795918 0.85714286 0.93877551 0.85714286 0.87755102 0.83673469 0.89583333 0.81632653] mean value: 0.8610119047619047 key: train_recall value: [0.89318182 0.90681818 0.90227273 0.89318182 0.9 0.90681818 0.90909091 0.91136364 0.89795918 0.90909091] mean value: 0.902977736549165 key: test_accuracy value: [0.84693878 0.84693878 0.86734694 0.83673469 0.89795918 0.82653061 0.84693878 0.78571429 0.89690722 0.81443299] mean value: 0.8466442247001895 key: train_accuracy value: [0.9125 0.91136364 0.91136364 0.90909091 0.91363636 0.91590909 0.91136364 0.91477273 0.90805902 0.91600454] mean value: 0.9124063564131669 key: test_roc_auc value: [0.84693878 0.84693878 0.86734694 0.83673469 0.89795918 0.82653061 0.84693878 0.78571429 0.89689626 0.81441327] mean value: 0.8466411564625851 key: train_roc_auc value: [0.9125 0.91136364 0.91136364 0.90909091 0.91363636 0.91590909 0.91136364 0.91477273 0.9080705 0.9159967 ] mean value: 0.9124067202638633 key: test_jcc value: [0.72727273 0.72727273 0.77192982 0.72413793 0.82142857 0.71186441 0.74137931 0.66129032 0.81132075 0.68965517] mean value: 0.7387551748405821 key: train_jcc value: [0.83617021 0.83647799 0.83578947 0.83086681 0.83898305 0.8435518 0.83682008 0.84243697 0.83018868 0.84388186] mean value: 0.8375166923627463 MCC on Blind test: 0.26 MCC on Training: 0.69 Running classifier: 22 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.07282376 0.04472232 0.05754161 0.05742645 0.05620623 0.05882907 0.05118728 0.05324006 0.05781031 0.05613136] mean value: 0.05659184455871582 key: score_time value: [0.02244902 0.01910925 0.02207398 0.02158642 0.0216136 0.02024913 0.02137351 0.0202384 0.0210681 0.02094817] mean value: 0.02107095718383789 key: test_mcc value: [0.71488145 0.69751845 0.65319726 0.81786082 0.71488145 0.75510204 0.69751845 0.65648795 0.81454707 0.56832741] mean value: 0.7090322359277383 key: train_mcc value: [0.80273933 0.81212116 0.80454545 0.79103985 0.80003306 0.78880139 0.78639613 0.80683694 0.78227276 0.81405267] mean value: 0.7988838743698872 key: test_fscore value: [0.85416667 0.83870968 0.82828283 0.90526316 0.86 0.87755102 0.85436893 0.83495146 0.90526316 0.77894737] mean value: 0.8537504265337054 key: train_fscore value: [0.89942197 0.90360046 0.90227273 0.89449541 0.89954338 0.89322618 0.89269406 0.90307868 0.88990826 0.90574713] mean value: 0.898398825036792 key: test_precision value: [0.87234043 0.88636364 0.82 0.93478261 0.84313725 0.87755102 0.81481481 0.7962963 0.91489362 0.80434783] mean value: 0.8564527500120672 key: train_precision value: [0.91529412 0.9239905 0.90227273 0.90277778 0.90366972 0.9025522 0.89678899 0.90617849 0.90023202 0.91627907] mean value: 0.9070035619314023 key: test_recall value: [0.83673469 0.79591837 0.83673469 0.87755102 0.87755102 0.87755102 0.89795918 0.87755102 0.89583333 0.75510204] mean value: 0.8528486394557824 key: train_recall value: [0.88409091 0.88409091 0.90227273 0.88636364 0.89545455 0.88409091 0.88863636 0.9 0.87981859 0.89545455] mean value: 0.8900273139558855 key: test_accuracy value: [0.85714286 0.84693878 0.82653061 0.90816327 0.85714286 0.87755102 0.84693878 0.82653061 0.90721649 0.78350515] mean value: 0.8537660424994741 key: train_accuracy value: [0.90113636 0.90568182 0.90227273 0.89545455 0.9 0.89431818 0.89318182 0.90340909 0.89103292 0.90692395] mean value: 0.8993411412650915 key: test_roc_auc value: [0.85714286 0.84693878 0.82653061 0.90816327 0.85714286 0.87755102 0.84693878 0.82653061 0.90710034 0.78380102] mean value: 0.853784013605442 key: train_roc_auc value: [0.90113636 0.90568182 0.90227273 0.89545455 0.9 0.89431818 0.89318182 0.90340909 0.89104566 0.90691095] mean value: 0.8993411152339723 key: test_jcc value: [0.74545455 0.72222222 0.70689655 0.82692308 0.75438596 0.78181818 0.74576271 0.71666667 0.82692308 0.63793103] mean value: 0.7464984032991354 key: train_jcc value: [0.81722689 0.82415254 0.82194617 0.80912863 0.81742739 0.80705394 0.80618557 0.82328482 0.80165289 0.82773109] mean value: 0.8155789936701755 MCC on Blind test: 0.27 MCC on Training: 0.71 Running classifier: 23 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=12, random_state=42) Running model pipeline: /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:463: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_CV['source_data'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:490: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_BT['source_data'] = 'BT' Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.03019309 0.03050923 0.03828621 0.03084159 0.03298759 0.03459835 0.04382086 0.02967453 0.03762603 0.03822613] mean value: 0.03467636108398438 key: score_time value: [0.01032114 0.01133108 0.01186895 0.01207161 0.01189947 0.01191902 0.01188993 0.01186132 0.01207399 0.01214957] mean value: 0.01173861026763916 key: test_mcc value: [0.65252314 0.73484692 0.71970161 0.7125253 0.59988588 0.66436384 0.46225016 0.36893239 0.40648783 0.49763461] mean value: 0.5819151683985548 key: train_mcc value: [0.67213974 0.74622584 0.71706748 0.56022506 0.71245425 0.71126518 0.70588373 0.55628305 0.45844661 0.62394517] mean value: 0.6463936109151318 key: test_fscore value: [0.83636364 0.86597938 0.86538462 0.85964912 0.77777778 0.80898876 0.76033058 0.72868217 0.73282443 0.77586207] mean value: 0.8011842543322756 key: train_fscore value: [0.84475806 0.87555556 0.86187845 0.79487179 0.83185841 0.83541147 0.85856574 0.79238441 0.75471698 0.82273603] mean value: 0.8272736901560883 key: test_precision value: [0.75409836 0.875 0.81818182 0.75384615 0.85365854 0.9 0.63888889 0.5875 0.57831325 0.67164179] mean value: 0.7431128802214789 key: train_precision value: [0.75905797 0.85652174 0.83870968 0.66564417 0.93732194 0.92541436 0.7641844 0.65912519 0.60689655 0.71404682] mean value: 0.7726922821472931 key: test_recall value: [0.93877551 0.85714286 0.91836735 1. 0.71428571 0.73469388 0.93877551 0.95918367 1. 0.91836735] mean value: 0.8979591836734695 key: train_recall value: [0.95227273 0.89545455 0.88636364 0.98636364 0.74772727 0.76136364 0.97954545 0.99318182 0.99773243 0.97045455] mean value: 0.9170459699031127 key: test_accuracy value: [0.81632653 0.86734694 0.85714286 0.83673469 0.79591837 0.82653061 0.70408163 0.64285714 0.63917526 0.73195876] mean value: 0.7718072796128761 key: train_accuracy value: [0.825 0.87272727 0.85795455 0.74545455 0.84886364 0.85 0.83863636 0.73977273 0.6753689 0.79114642] mean value: 0.8044924414405118 key: test_roc_auc value: [0.81632653 0.86734694 0.85714286 0.83673469 0.79591837 0.82653061 0.70408163 0.64285714 0.64285714 0.73001701] mean value: 0.7719812925170069 key: train_roc_auc value: [0.825 0.87272727 0.85795455 0.74545455 0.84886364 0.85 0.83863636 0.73977273 0.67500258 0.79134972] mean value: 0.8044761389404247 key: test_jcc value: [0.71875 0.76363636 0.76271186 0.75384615 0.63636364 0.67924528 0.61333333 0.57317073 0.57831325 0.63380282] mean value: 0.6713173436225908 key: train_jcc value: [0.73123909 0.77865613 0.75728155 0.65957447 0.71212121 0.71734475 0.7521815 0.65615616 0.60606061 0.69885434] mean value: 0.7069469806571123 MCC on Blind test: 0.16 MCC on Training: 0.58 Running classifier: 24 Model_name: XGBoost Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.22737741 0.18665481 0.18517041 0.18880463 0.22297382 0.20119596 0.18789697 0.18880701 0.35931373 0.18221974] mean value: 0.21304144859313964 key: score_time value: [0.01144624 0.01173258 0.01169252 0.01128173 0.01246643 0.01135731 0.01204395 0.01173663 0.01250029 0.01230788] mean value: 0.011856555938720703 key: test_mcc value: [0.81649658 0.79591837 0.85732141 0.9797959 0.91913329 0.93897107 0.91913329 0.85875386 0.9383242 0.87696964] mean value: 0.8900817607937993 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.90721649 0.89795918 0.92783505 0.98989899 0.96 0.96907216 0.96 0.92631579 0.96842105 0.94 ] mean value: 0.9446718727017928 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.91666667 0.89795918 0.9375 0.98 0.94117647 0.97916667 0.94117647 0.95652174 0.9787234 0.92156863] mean value: 0.9450459229020008 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.89795918 0.89795918 0.91836735 1. 0.97959184 0.95918367 0.97959184 0.89795918 0.95833333 0.95918367] mean value: 0.9448129251700681 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.90816327 0.89795918 0.92857143 0.98979592 0.95918367 0.96938776 0.95918367 0.92857143 0.96907216 0.93814433] mean value: 0.9448032821375973 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.90816327 0.89795918 0.92857143 0.98979592 0.95918367 0.96938776 0.95918367 0.92857143 0.96896259 0.93792517] mean value: 0.9447704081632653 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.83018868 0.81481481 0.86538462 0.98 0.92307692 0.94 0.92307692 0.8627451 0.93877551 0.88679245] mean value: 0.8964855016672045 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.42 MCC on Training: 0.89 Extracting tts_split_name: 70_30 Total cols in each df: CV df: 8 metaDF: 17 Adding column: Model_name Total cols in bts df: BT_df: 8 First proceeding to rowbind CV and BT dfs: Final output should have: 25 columns Combinig 2 using pd.concat by row ~ rowbind Checking Dims of df to combine: Dim of CV: (24, 8) Dim of BT: (24, 8) 8 [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... 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[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... 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Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.6s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.6s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.6s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished Number of Common columns: 8 These are: ['source_data', 'Precision', 'JCC', 'MCC', 'Recall', 'Accuracy', 'F1', 'ROC_AUC'] Concatenating dfs with different resampling methods [WF]: Split type: 70_30 No. of dfs combining: 2 PASS: 2 dfs successfully combined nrows in combined_df_wf: 48 ncols in combined_df_wf: 8 PASS: proceeding to merge metadata with CV and BT dfs Adding column: Model_name ========================================================= SUCCESS: Ran multiple classifiers ======================================================= ============================================================== Running several classification models (n): 24 List of models: ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('Gaussian NB', GaussianNB()) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('K-Nearest Neighbors', KNeighborsClassifier()) ('LDA', LinearDiscriminantAnalysis()) ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(cv=3, random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Multinomial', MultinomialNB()) ('Naive Bayes', BernoulliNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=12, random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Random Forest', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42)) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=3)) ('SVC', SVC(random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=12, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ================================================================ Running classifier: 1 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.24886513 0.24540758 0.24066377 0.24255037 0.24011874 0.2441783 0.24653459 0.23997498 0.24087024 0.2422936 ] mean value: 0.24314572811126708 key: score_time value: [0.01550961 0.01638079 0.01613736 0.01550126 0.01556039 0.01611161 0.01551843 0.01545525 0.01552367 0.01566505] mean value: 0.01573634147644043 key: test_mcc value: [0.63318071 0.83673469 0.63318071 0.79591837 0.8247861 0.65982888 0.6951817 0.58131836 0.77754765 0.69081988] mean value: 0.7128497048887676 key: train_mcc value: [0.88791517 0.85779572 0.82959902 0.86840613 0.8932746 0.84616289 0.87538216 0.87449975 0.80703979 0.87360706] mean value: 0.8613682294229777 key: test_fscore value: [0.82 0.91836735 0.82 0.89795918 0.91428571 0.83809524 0.85148515 0.80373832 0.89108911 0.85454545] mean value: 0.8609565512721403 key: train_fscore value: [0.94481236 0.93007769 0.91428571 0.93483146 0.94736842 0.92427617 0.93840985 0.93832599 0.90351873 0.9376392 ] mean value: 0.931354559131073 key: test_precision value: [0.80392157 0.91836735 0.80392157 0.89795918 0.85714286 0.78571429 0.82692308 0.74137931 0.8490566 0.7704918 ] mean value: 0.8254877605044466 key: train_precision value: [0.91845494 0.90889371 0.91954023 0.92444444 0.91525424 0.90611354 0.92494481 0.91025641 0.90454545 0.91921397] mean value: 0.9151661744648429 key: test_recall value: [0.83673469 0.91836735 0.83673469 0.89795918 0.97959184 0.89795918 0.87755102 0.87755102 0.9375 0.95918367] mean value: 0.9019132653061226 key: train_recall value: [0.97272727 0.95227273 0.90909091 0.94545455 0.98181818 0.94318182 0.95227273 0.96818182 0.90249433 0.95681818] mean value: 0.9484312512883942 key: test_accuracy value: [0.81632653 0.91836735 0.81632653 0.89795918 0.90816327 0.82653061 0.84693878 0.78571429 0.88659794 0.83505155] mean value: 0.8537976015148327 key: train_accuracy value: [0.94318182 0.92840909 0.91477273 0.93409091 0.94545455 0.92272727 0.9375 0.93636364 0.90351873 0.93643587] mean value: 0.9302454597048809 key: test_roc_auc value: [0.81632653 0.91836735 0.81632653 0.89795918 0.90816327 0.82653061 0.84693878 0.78571429 0.88711735 0.8337585 ] mean value: 0.8537202380952381 key: train_roc_auc value: [0.94318182 0.92840909 0.91477273 0.93409091 0.94545455 0.92272727 0.9375 0.93636364 0.90351989 0.93645898] mean value: 0.9302478870336014 key: test_jcc value: [0.69491525 0.8490566 0.69491525 0.81481481 0.84210526 0.72131148 0.74137931 0.671875 0.80357143 0.74603175] mean value: 0.7579976150578709 key: train_jcc value: [0.89539749 0.86929461 0.84210526 0.87763713 0.9 0.85921325 0.88396624 0.88381743 0.82401656 0.88259958] mean value: 0.8718047555797475 MCC on Blind test: 0.31 MCC on Training: 0.71 Running classifier: 2 Model_name: Bagging Classifier Model func: BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3))]) key: fit_time value: [0.49697495 0.54915786 0.53560328 0.5104301 0.52162504 0.54068398 0.57823777 0.50490713 0.47251439 0.49453473] [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... 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Building estimator 1 of 8 for this parallel run (total 100)... 6b7b8bBuilding estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... 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Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 2.4s remaining: 4.7s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 2.4s remaining: 4.8s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 2.4s remaining: 4.9s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 2.4s remaining: 4.9s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 2.4s remaining: 4.9s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 2.5s remaining: 5.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 2.5s remaining: 0.8s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 2.5s remaining: 0.8s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 2.5s remaining: 5.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 2.5s remaining: 0.8s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 2.6s remaining: 5.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 2.6s remaining: 5.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 2.6s remaining: 5.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 2.6s remaining: 0.9s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 2.6s remaining: 0.9s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 2.6s remaining: 0.9s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 2.6s remaining: 0.9s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 2.6s remaining: 0.9s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 2.6s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 2.6s remaining: 0.9s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 2.6s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 2.7s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 2.7s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 2.7s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 2.7s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 2.7s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 2.7s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 2.7s remaining: 0.9s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 2.7s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 2.7s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.6s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished mean value: 0.5204669237136841 key: score_time value: [0.06498861 0.06081343 0.04414153 0.07324767 0.06373596 0.08278871 0.0591805 0.05359077 0.08870387 0.0712285 ] mean value: 0.06624195575714112 key: test_mcc value: [0.91836735 0.94053994 0.94053994 1. 0.90267093 0.9797959 0.95998366 0.94053994 0.95959175 0.88291871] mean value: 0.9424948130640054 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.95918367 0.97029703 0.97029703 1. 0.95145631 0.98989899 0.98 0.97029703 0.97959184 0.94230769] mean value: 0.9713329592199285 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.95918367 0.94230769 0.94230769 1. 0.90740741 0.98 0.96078431 0.94230769 0.96 0.89090909] mean value: 0.9485207562434453 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.95918367 1. 1. 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9959183673469388 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.95918367 0.96938776 0.96938776 1. 0.94897959 0.98979592 0.97959184 0.96938776 0.97938144 0.93814433] mean value: 0.9703240058910161 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.95918367 0.96938776 0.96938776 1. 0.94897959 0.98979592 0.97959184 0.96938776 0.97959184 0.9375 ] mean value: 0.9702806122448979 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.92156863 0.94230769 0.94230769 1. 0.90740741 0.98 0.96078431 0.94230769 0.96 0.89090909] mean value: 0.9447592516416045 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.32 MCC on Training: 0.94 Running classifier: 3 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.04375625 0.04277849 0.04280972 0.04306149 0.04375553 0.04066491 0.04364395 0.0419476 0.04068279 0.04261303] mean value: 0.04257137775421142 key: score_time value: [0.00875735 0.00882149 0.00924683 0.00891042 0.00901413 0.00907612 0.00970793 0.00891876 0.00883532 0.00910425] mean value: 0.00903925895690918 key: test_mcc value: [0.87828292 0.90267093 0.84811452 0.9797959 0.84811452 0.84811452 0.88420483 0.84811452 0.82911571 0.77618233] mean value: 0.8642710728600559 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.94 0.95145631 0.9245283 0.98989899 0.9245283 0.9245283 0.94230769 0.9245283 0.91428571 0.89090909] mean value: 0.932697100562827 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.92156863 0.90740741 0.85964912 0.98 0.85964912 0.85964912 0.89090909 0.85964912 0.84210526 0.80327869] mean value: 0.8783865568678033 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.95918367 1. 1. 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9959183673469388 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.93877551 0.94897959 0.91836735 0.98979592 0.91836735 0.91836735 0.93877551 0.91836735 0.90721649 0.87628866] mean value: 0.9273301073006524 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.93877551 0.94897959 0.91836735 0.98979592 0.91836735 0.91836735 0.93877551 0.91836735 0.90816327 0.875 ] mean value: 0.9272959183673469 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.88679245 0.90740741 0.85964912 0.98 0.85964912 0.85964912 0.89090909 0.85964912 0.84210526 0.80327869] mean value: 0.8749089394057241 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.14 MCC on Training: 0.86 Running classifier: 4 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.0113287 0.01121712 0.0113585 0.01137066 0.01124907 0.01134896 0.01130271 0.01167727 0.0116148 0.01166677] mean value: 0.01141345500946045 key: score_time value: [0.00877357 0.00875354 0.0087676 0.00879431 0.00874996 0.00874782 0.00875497 0.00884628 0.00902224 0.00906467] mean value: 0.008827495574951171 key: test_mcc value: [0.76537164 0.79582243 0.8660254 0.90267093 0.84811452 0.79582243 0.81302949 0.81302949 0.8468773 0.9016018 ] mean value: 0.8348365434579395 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.88679245 0.89908257 0.93333333 0.95145631 0.9245283 0.89908257 0.90740741 0.90740741 0.92307692 0.95145631] mean value: 0.9183623584915954 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.8245614 0.81666667 0.875 0.90740741 0.85964912 0.81666667 0.83050847 0.83050847 0.85714286 0.90740741] mean value: 0.8525518480759338 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.95918367 1. 1. 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9959183673469388 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.87755102 0.8877551 0.92857143 0.94897959 0.91836735 0.8877551 0.89795918 0.89795918 0.91752577 0.94845361] mean value: 0.9110877340626973 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.87755102 0.8877551 0.92857143 0.94897959 0.91836735 0.8877551 0.89795918 0.89795918 0.91836735 0.94791667] mean value: 0.9111181972789115 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.79661017 0.81666667 0.875 0.90740741 0.85964912 0.81666667 0.83050847 0.83050847 0.85714286 0.90740741] mean value: 0.849756724674209 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.13 MCC on Training: 0.83 Running classifier: 5 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.16264081 0.16409802 0.16428351 0.16683292 0.17106748 0.16559148 0.16545486 0.16364145 0.16757488 0.15864038] mean value: 0.16498258113861083 key: score_time value: [0.01964974 0.01904273 0.01933193 0.01992011 0.01879287 0.019454 0.01871777 0.02072477 0.01932049 0.01924825] mean value: 0.019420266151428223 key: test_mcc value: [0.95998366 0.95998366 0.9797959 0.95998366 0.9797959 0.9797959 0.9797959 1. 1. 0.97958324] mean value: 0.9778717813146617 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.97916667 0.98 0.98989899 0.98 0.98989899 0.98989899 0.98989899 1. 1. 0.98989899] mean value: 0.9888661616161617 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.96078431 0.98 0.96078431 0.98 0.98 0.98 1. 1. 0.98 ] mean value: 0.9821568627450981 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.95918367 1. 1. 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9959183673469388 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.97959184 0.97959184 0.98979592 0.97959184 0.98979592 0.98979592 0.98979592 1. 1. 0.98969072] mean value: 0.9887649905322954 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.97959184 0.97959184 0.98979592 0.97959184 0.98979592 0.98979592 0.98979592 1. 1. 0.98958333] mean value: 0.9887542517006803 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.95918367 0.96078431 0.98 0.96078431 0.98 0.98 0.98 1. 1. 0.98 ] mean value: 0.9780752300920369 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.09 MCC on Training: 0.98 Running classifier: 6 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [1.02572823 1.13424754 1.10653305 1.0519321 1.02863383 1.03936625 1.05998826 1.08891344 1.07116222 1.0954473 ] mean value: 1.0701952219009399 key: score_time value: [0.0092268 0.01008391 0.01050115 0.00928521 0.00937963 0.00942874 0.00941849 0.0104785 0.01009274 0.01022315] mean value: 0.009811830520629884 key: test_mcc value: [0.85875386 0.91913329 0.90267093 1. 0.8660254 0.85875386 0.88048967 0.90267093 0.95959175 0.86452058] mean value: 0.901261028263502 key: train_mcc value: [0.98421547 0.98181818 0.9887002 0.98198051 0.9887002 0.97956822 0.98411378 0.9887002 0.9887128 0.98871309] mean value: 0.9855222655818897 key: test_fscore value: [0.93069307 0.96 0.95145631 1. 0.93333333 0.93069307 0.94117647 0.95145631 0.97959184 0.93333333] mean value: 0.9511733733962681 key: train_fscore value: [0.99210823 0.99090909 0.99435028 0.99099099 0.99435028 0.98980747 0.99207248 0.99435028 0.99436302 0.99435028] mean value: 0.9927652417952716 key: test_precision value: [0.90384615 0.94117647 0.90740741 1. 0.875 0.90384615 0.90566038 0.90740741 0.96 0.875 ] mean value: 0.917934397045385 key: train_precision value: [0.98434004 0.99090909 0.98876404 0.98214286 0.98876404 0.98645598 0.98871332 0.98876404 0.98878924 0.98876404] mean value: 0.9876406710463854 key: test_recall value: [0.95918367 0.97959184 1. 1. 1. 0.95918367 0.97959184 1. 1. 1. ] mean value: 0.9877551020408163 key: train_recall value: [1. 0.99090909 1. 1. 1. 0.99318182 0.99545455 1. 1. 1. ] mean value: 0.9979545454545455 key: test_accuracy value: [0.92857143 0.95918367 0.94897959 1. 0.92857143 0.92857143 0.93877551 0.94897959 0.97938144 0.92783505] mean value: 0.9488849147906585 key: train_accuracy value: [0.99204545 0.99090909 0.99431818 0.99090909 0.99431818 0.98977273 0.99204545 0.99431818 0.99432463 0.99432463] mean value: 0.9927285625838407 key: test_roc_auc value: [0.92857143 0.95918367 0.94897959 1. 0.92857143 0.92857143 0.93877551 0.94897959 0.97959184 0.92708333] mean value: 0.9488307823129253 key: train_roc_auc value: [0.99204545 0.99090909 0.99431818 0.99090909 0.99431818 0.98977273 0.99204545 0.99431818 0.99431818 0.99433107] mean value: 0.9927285611214183 key: test_jcc value: [0.87037037 0.92307692 0.90740741 1. 0.875 0.87037037 0.88888889 0.90740741 0.96 0.875 ] mean value: 0.9077521367521367 key: train_jcc value: [0.98434004 0.98198198 0.98876404 0.98214286 0.98876404 0.97982063 0.98426966 0.98876404 0.98878924 0.98876404] mean value: 0.985640059203505 MCC on Blind test: 0.37 MCC on Training: 0.9 Running classifier: 7 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.0113945 0.01175904 0.01185489 0.01119256 0.01106739 0.0138154 0.01286721 0.01277351 0.0127542 0.01278043] mean value: 0.012225914001464843 key: score_time value: [0.00927234 0.00957561 0.00981045 0.00939155 0.00999308 0.0115447 0.0101676 0.00951457 0.01007795 0.00950909] mean value: 0.009885692596435547 key: test_mcc value: [0.2857738 0.4607393 0.34810057 0.43440012 0.55147997 0.56286657 0.53339646 0.22467703 0.45381998 0.25818803] mean value: 0.4113441828700594 key: train_mcc value: [0.38120403 0.42601725 0.45371043 0.45607455 0.4635827 0.45119482 0.462369 0.44835883 0.44202755 0.44833325] mean value: 0.4432872409936642 key: test_fscore value: [0.63917526 0.68965517 0.65957447 0.68888889 0.77083333 0.75 0.75268817 0.60416667 0.68235294 0.625 ] mean value: 0.6862334900339229 key: train_fscore value: [0.67461263 0.67839196 0.7147929 0.71563981 0.71684588 0.71462264 0.72150411 0.7060241 0.71461717 0.7001224 ] mean value: 0.7057173600956015 key: test_precision value: [0.64583333 0.78947368 0.68888889 0.75609756 0.78723404 0.84615385 0.79545455 0.61702128 0.78378378 0.63829787] mean value: 0.7348238834289896 key: train_precision value: [0.70927318 0.75842697 0.74567901 0.74752475 0.75566751 0.74264706 0.74695864 0.75128205 0.73159145 0.75862069] mean value: 0.744767130652914 key: test_recall value: [0.63265306 0.6122449 0.63265306 0.63265306 0.75510204 0.67346939 0.71428571 0.59183673 0.60416667 0.6122449 ] mean value: 0.6461309523809524 key: train_recall value: [0.64318182 0.61363636 0.68636364 0.68636364 0.68181818 0.68863636 0.69772727 0.66590909 0.6984127 0.65 ] mean value: 0.6712049062049064 key: test_accuracy value: [0.64285714 0.7244898 0.67346939 0.71428571 0.7755102 0.7755102 0.76530612 0.6122449 0.72164948 0.62886598] mean value: 0.7034188933305281 key: train_accuracy value: [0.68977273 0.70909091 0.72613636 0.72727273 0.73068182 0.725 0.73068182 0.72272727 0.72077185 0.72190692] mean value: 0.7204042410483955 key: test_roc_auc value: [0.64285714 0.7244898 0.67346939 0.71428571 0.7755102 0.7755102 0.76530612 0.6122449 0.72045068 0.62903912] mean value: 0.7033163265306123 key: train_roc_auc value: [0.68977273 0.70909091 0.72613636 0.72727273 0.73068182 0.725 0.73068182 0.72272727 0.72079726 0.7218254 ] mean value: 0.7203986291486292 key: test_jcc value: [0.46969697 0.52631579 0.49206349 0.52542373 0.62711864 0.6 0.60344828 0.43283582 0.51785714 0.45454545] mean value: 0.524930531827569 key: train_jcc value: [0.50899281 0.51330798 0.55616943 0.55719557 0.55865922 0.5559633 0.56433824 0.54562384 0.55595668 0.5386064 ] mean value: 0.5454813465363275 MCC on Blind test: 0.23 MCC on Training: 0.41 Running classifier: 8 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.50937057 0.44908667 0.43795538 0.5104754 0.36221075 0.36845326 0.38172603 0.38276863 0.40464997 0.428298 ] mean value: 0.4234994649887085 key: score_time value: [0.03986335 0.03731322 0.02118754 0.02162504 0.02096844 0.02097273 0.02092671 0.02092981 0.03322721 0.03722525] mean value: 0.027423930168151856 key: test_mcc value: [0.79658219 0.84307902 0.88420483 0.8660254 0.84307902 0.78354679 0.78893206 0.74740932 0.92071912 0.82850613] mean value: 0.8302083897125921 key: train_mcc value: [0.95711733 0.94827847 0.95737467 0.95957677 0.94827847 0.9503535 0.94388658 0.95479208 0.94599235 0.94867189] mean value: 0.9514322119767439 key: test_fscore value: [0.9 0.92307692 0.94230769 0.93333333 0.92307692 0.8952381 0.89719626 0.87850467 0.96 0.91588785] mean value: 0.9168621752079696 key: train_fscore value: [0.97867565 0.97430168 0.97877095 0.97986577 0.97430168 0.97533632 0.97212932 0.97752809 0.97321429 0.97441602] mean value: 0.9758539755055212 key: test_precision value: [0.88235294 0.87272727 0.89090909 0.875 0.87272727 0.83928571 0.82758621 0.81034483 0.92307692 0.84482759] mean value: 0.8638837835592399 key: train_precision value: [0.96674058 0.95824176 0.96263736 0.96475771 0.95824176 0.96238938 0.95404814 0.96666667 0.95824176 0.95424837] mean value: 0.9606213476364889 key: test_recall value: [0.91836735 0.97959184 1. 1. 0.97959184 0.95918367 0.97959184 0.95918367 1. 1. ] mean value: 0.9775510204081632 key: train_recall value: [0.99090909 0.99090909 0.99545455 0.99545455 0.99090909 0.98863636 0.99090909 0.98863636 0.98866213 0.99545455] mean value: 0.9915934858792003 key: test_accuracy value: [0.89795918 0.91836735 0.93877551 0.92857143 0.91836735 0.8877551 0.8877551 0.86734694 0.95876289 0.90721649] mean value: 0.9110877340626973 key: train_accuracy value: [0.97840909 0.97386364 0.97840909 0.97954545 0.97386364 0.975 0.97159091 0.97727273 0.97275823 0.9738933 ] mean value: 0.9754606077804148 key: test_roc_auc value: [0.89795918 0.91836735 0.93877551 0.92857143 0.91836735 0.8877551 0.8877551 0.86734694 0.95918367 0.90625 ] mean value: 0.9110331632653061 key: train_roc_auc value: [0.97840909 0.97386364 0.97840909 0.97954545 0.97386364 0.975 0.97159091 0.97727273 0.97274016 0.97391775] mean value: 0.9754612451041023 key: test_jcc value: [0.81818182 0.85714286 0.89090909 0.875 0.85714286 0.81034483 0.81355932 0.78333333 0.92307692 0.84482759] mean value: 0.8473518615613882 key: train_jcc value: [0.95824176 0.94989107 0.95842451 0.96052632 0.94989107 0.95185996 0.94577007 0.95604396 0.94782609 0.95010846] mean value: 0.9528583240948698 MCC on Blind test: 0.18 MCC on Training: 0.83 Running classifier: 9 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.0149951 0.01231313 0.01211143 0.01062036 0.0114367 0.01019597 0.01149464 0.01151299 0.01208854 0.01228762] mean value: 0.011905646324157715 key: score_time value: [0.052109 0.0144279 0.01762128 0.0151844 0.01511264 0.01414442 0.01716638 0.01947832 0.01641107 0.01972246] mean value: 0.020137786865234375 key: test_mcc value: [0.59988588 0.66934195 0.59988588 0.63632612 0.73671967 0.67733401 0.49660854 0.45342519 0.77722689 0.6482285 ] mean value: 0.6294982641557125 key: train_mcc value: [0.7775812 0.78089715 0.7696613 0.76909685 0.77673462 0.77673462 0.7793831 0.77598081 0.77483312 0.7827684 ] mean value: 0.776367116881495 key: test_fscore value: [0.81132075 0.84210526 0.81132075 0.82758621 0.87272727 0.84684685 0.77192982 0.75438596 0.88888889 0.83478261] mean value: 0.8261894386120755 key: train_fscore value: [0.89092762 0.89300412 0.88797533 0.88708037 0.8907048 0.8907048 0.89256198 0.89119171 0.88956434 0.89344262] mean value: 0.8907157694616877 key: test_precision value: [0.75438596 0.73846154 0.75438596 0.71641791 0.78688525 0.75806452 0.67692308 0.66153846 0.8 0.72727273] mean value: 0.73743354064988 key: train_precision value: [0.8077634 0.81578947 0.81050657 0.80294659 0.80890538 0.80890538 0.81818182 0.81904762 0.8040293 0.81343284] mean value: 0.8109508372146775 key: test_recall value: [0.87755102 0.97959184 0.87755102 0.97959184 0.97959184 0.95918367 0.89795918 0.87755102 1. 0.97959184] mean value: 0.9408163265306122 key: train_recall value: [0.99318182 0.98636364 0.98181818 0.99090909 0.99090909 0.99090909 0.98181818 0.97727273 0.99546485 0.99090909] mean value: 0.9879555761698617 key: test_accuracy value: [0.79591837 0.81632653 0.79591837 0.79591837 0.85714286 0.82653061 0.73469388 0.71428571 0.87628866 0.80412371] mean value: 0.8017147065011573 key: train_accuracy value: [0.87840909 0.88181818 0.87613636 0.87386364 0.87840909 0.87840909 0.88181818 0.88068182 0.87627696 0.88195233] mean value: 0.8787774739448974 key: test_roc_auc value: [0.79591837 0.81632653 0.79591837 0.79591837 0.85714286 0.82653061 0.73469388 0.71428571 0.87755102 0.80229592] mean value: 0.8016581632653061 key: train_roc_auc value: [0.87840909 0.88181818 0.87613636 0.87386364 0.87840909 0.87840909 0.88181818 0.88068182 0.87614152 0.88207586] mean value: 0.878776283240569 key: test_jcc value: [0.68253968 0.72727273 0.68253968 0.70588235 0.77419355 0.734375 0.62857143 0.6056338 0.8 0.71641791] mean value: 0.7057426135516457 key: train_jcc value: [0.80330882 0.80669145 0.79852126 0.79707495 0.80294659 0.80294659 0.80597015 0.80373832 0.80109489 0.80740741] mean value: 0.8029700435504088 MCC on Blind test: 0.13 MCC on Training: 0.63 Running classifier: 10 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.0487237 0.05516791 0.08636856 0.0776751 0.06776142 0.09049654 0.09448886 0.0914588 0.05655217 0.08114386] mean value: 0.07498369216918946 key: score_time value: [0.01226115 0.01217985 0.03317285 0.02349114 0.02939105 0.01385498 0.01259995 0.01237226 0.01217985 0.01249385] mean value: 0.017399692535400392 key: test_mcc value: [0.53072278 0.59632419 0.53160953 0.57442696 0.69751845 0.517143 0.49487166 0.36927447 0.69076995 0.62361955] mean value: 0.5626280533074282 key: train_mcc value: [0.69571333 0.71644403 0.70577873 0.73456274 0.71121745 0.7289691 0.71769256 0.74656597 0.71822567 0.72713628] mean value: 0.7202305873396903 key: test_fscore value: [0.76767677 0.80769231 0.77227723 0.7961165 0.85436893 0.77358491 0.76190476 0.69902913 0.84210526 0.82568807] mean value: 0.7900443870316173 key: train_fscore value: [0.84977578 0.8606466 0.85651214 0.87145969 0.85995624 0.86813187 0.86278814 0.87610619 0.86338798 0.86717892] mean value: 0.8635943567242694 key: test_precision value: [0.76 0.76363636 0.75 0.75925926 0.81481481 0.71929825 0.71428571 0.66666667 0.85106383 0.75 ] mean value: 0.7549024894064088 key: train_precision value: [0.83849558 0.84463895 0.83261803 0.83682008 0.82911392 0.84042553 0.8343949 0.85344828 0.83333333 0.83864119] mean value: 0.838192979290528 key: test_recall value: [0.7755102 0.85714286 0.79591837 0.83673469 0.89795918 0.83673469 0.81632653 0.73469388 0.83333333 0.91836735] mean value: 0.8302721088435374 key: train_recall value: [0.86136364 0.87727273 0.88181818 0.90909091 0.89318182 0.89772727 0.89318182 0.9 0.89569161 0.89772727] mean value: 0.8907055246340961 key: test_accuracy value: [0.76530612 0.79591837 0.76530612 0.78571429 0.84693878 0.75510204 0.74489796 0.68367347 0.84536082 0.80412371] mean value: 0.7792341678939616 key: train_accuracy value: [0.84772727 0.85795455 0.85227273 0.86590909 0.85454545 0.86363636 0.85795455 0.87272727 0.85811578 0.86265607] mean value: 0.8593499122897533 key: test_roc_auc value: [0.76530612 0.79591837 0.76530612 0.78571429 0.84693878 0.75510204 0.74489796 0.68367347 0.8452381 0.80293367] mean value: 0.7791028911564626 key: train_roc_auc value: [0.84772727 0.85795455 0.85227273 0.86590909 0.85454545 0.86363636 0.85795455 0.87272727 0.85807308 0.86269584] mean value: 0.8593496186353329 key: test_jcc value: [0.62295082 0.67741935 0.62903226 0.66129032 0.74576271 0.63076923 0.61538462 0.53731343 0.72727273 0.703125 ] mean value: 0.6550320473282804 key: train_jcc value: [0.73879142 0.7553816 0.74903475 0.77220077 0.75431862 0.76699029 0.75868726 0.77952756 0.75961538 0.76550388] mean value: 0.760005153656526 MCC on Blind test: 0.23 MCC on Training: 0.56 Running classifier: 11 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.04561901 0.04539037 0.04575133 0.04643393 0.04550409 0.04538894 0.04456043 0.04629302 0.04669333 0.04602528] mean value: 0.04576597213745117 key: score_time value: [0.01287937 0.01220417 0.01320171 0.01213598 0.01315808 0.01310921 0.01317263 0.01322389 0.01241279 0.01317215] mean value: 0.012866997718811035 key: test_mcc value: [0.55286561 0.55286561 0.49071649 0.53979562 0.59381861 0.40824829 0.45133547 0.22619193 0.63066331 0.48884787] mean value: 0.4935348805785834 key: train_mcc value: [0.62058438 0.61379491 0.63456472 0.57331985 0.6136427 0.5954607 0.61610165 0.64096869 0.61002556 0.61867801] mean value: 0.6137141165358168 key: test_fscore value: [0.76595745 0.76595745 0.75247525 0.74157303 0.80392157 0.70707071 0.73786408 0.63461538 0.80434783 0.76190476] mean value: 0.7475687500824804 key: train_fscore value: [0.81214848 0.80898876 0.82051282 0.78139535 0.80637813 0.79818594 0.81032548 0.82167043 0.80888889 0.81038375] mean value: 0.8078878029949597 key: test_precision value: [0.8 0.8 0.73076923 0.825 0.77358491 0.7 0.7037037 0.6 0.84090909 0.71428571] mean value: 0.7488252645328117 key: train_precision value: [0.80400891 0.8 0.80525164 0.8 0.80821918 0.79638009 0.80044346 0.8161435 0.79302832 0.80493274] mean value: 0.8028407833007742 key: test_recall value: [0.73469388 0.73469388 0.7755102 0.67346939 0.83673469 0.71428571 0.7755102 0.67346939 0.77083333 0.81632653] mean value: 0.7505527210884353 key: train_recall value: [0.82045455 0.81818182 0.83636364 0.76363636 0.80454545 0.8 0.82045455 0.82727273 0.82539683 0.81590909] mean value: 0.8132215007215006 key: test_accuracy value: [0.7755102 0.7755102 0.74489796 0.76530612 0.79591837 0.70408163 0.7244898 0.6122449 0.81443299 0.74226804] mean value: 0.7454660214601304 key: train_accuracy value: [0.81022727 0.80681818 0.81704545 0.78636364 0.80681818 0.79772727 0.80795455 0.82045455 0.80476731 0.8093076 ] mean value: 0.8067484005778557 key: test_roc_auc value: [0.7755102 0.7755102 0.74489796 0.76530612 0.79591837 0.70408163 0.7244898 0.6122449 0.8139881 0.7414966 ] mean value: 0.745344387755102 key: train_roc_auc value: [0.81022727 0.80681818 0.81704545 0.78636364 0.80681818 0.79772727 0.80795455 0.82045455 0.80474387 0.80931509] mean value: 0.806746804782519 key: test_jcc value: [0.62068966 0.62068966 0.6031746 0.58928571 0.67213115 0.546875 0.58461538 0.46478873 0.67272727 0.61538462] mean value: 0.5990361780467769 key: train_jcc value: [0.68371212 0.67924528 0.69565217 0.64122137 0.67557252 0.66415094 0.68113208 0.69731801 0.67910448 0.68121442] mean value: 0.6778323396668876 MCC on Blind test: 0.33 MCC on Training: 0.49 Running classifier: 12 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(cv=3, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(cv=3, random_state=42))]) key: fit_time value: [0.76042366 0.63972497 0.62521863 0.69791675 0.62907839 0.61978531 0.64644599 0.65545678 0.64951801 0.61893463] mean value: 0.6542503118515015 key: score_time value: [0.01240373 0.01248693 0.01255178 0.01244426 0.01336598 0.0132041 0.0181284 0.01325083 0.01312685 0.01312542] mean value: 0.013408827781677245 key: test_mcc value: [0.67403108 0.65648795 0.66436384 0.65428866 0.74740932 0.65428866 0.49827288 0.39609129 0.73214286 0.66029034] mean value: 0.6337666868617058 key: train_mcc value: [0.79998039 0.77625401 0.76497307 0.77588419 0.81245513 0.7717012 0.72112719 0.79684695 0.76305254 0.76268855] mean value: 0.7744963221310788 key: test_fscore value: [0.84 0.83495146 0.8411215 0.83168317 0.87850467 0.83168317 0.76635514 0.72222222 0.86597938 0.8411215 ] mean value: 0.8253622200348181 key: train_fscore value: [0.90251917 0.89060773 0.88520971 0.89012209 0.90869565 0.88839779 0.86318131 0.90066225 0.88448845 0.88372093] mean value: 0.8897605087512552 key: test_precision value: [0.82352941 0.7962963 0.77586207 0.80769231 0.81034483 0.80769231 0.70689655 0.66101695 0.85714286 0.77586207] mean value: 0.7822335646982396 key: train_precision value: [0.87103594 0.86666667 0.86051502 0.86984816 0.87083333 0.86451613 0.8453159 0.87553648 0.85897436 0.86177106] mean value: 0.8645013049592902 key: test_recall value: [0.85714286 0.87755102 0.91836735 0.85714286 0.95918367 0.85714286 0.83673469 0.79591837 0.875 0.91836735] mean value: 0.8752551020408165 key: train_recall value: [0.93636364 0.91590909 0.91136364 0.91136364 0.95 0.91363636 0.88181818 0.92727273 0.91156463 0.90681818] mean value: 0.9166110080395795 key: test_accuracy value: [0.83673469 0.82653061 0.82653061 0.82653061 0.86734694 0.82653061 0.74489796 0.69387755 0.86597938 0.82474227] mean value: 0.8139701241321271 key: train_accuracy value: [0.89886364 0.8875 0.88181818 0.8875 0.90454545 0.88522727 0.86022727 0.89772727 0.88081725 0.88081725] mean value: 0.8865043597151997 key: test_roc_auc value: [0.83673469 0.82653061 0.82653061 0.82653061 0.86734694 0.82653061 0.74489796 0.69387755 0.86607143 0.82376701] mean value: 0.8138818027210885 key: train_roc_auc value: [0.89886364 0.8875 0.88181818 0.8875 0.90454545 0.88522727 0.86022727 0.89772727 0.88078231 0.88084673] mean value: 0.8865038136466709 key: test_jcc value: [0.72413793 0.71666667 0.72580645 0.71186441 0.78333333 0.71186441 0.62121212 0.56521739 0.76363636 0.72580645] mean value: 0.7049545523972444 key: train_jcc value: [0.82235529 0.80278884 0.79405941 0.802 0.83266932 0.79920477 0.7592955 0.81927711 0.79289941 0.79166667] mean value: 0.8016216316470149 MCC on Blind test: 0.16 MCC on Training: 0.63 Running classifier: 13 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [3.99650836 3.9884901 3.77513695 4.43216109 3.27325678 3.81201768 3.92962384 3.69694853 3.60010862 3.84240675] mean value: 3.834665870666504 key: score_time value: [0.01265717 0.01298571 0.01266837 0.01260543 0.01369548 0.01269388 0.01315403 0.01283693 0.0128088 0.01288581] mean value: 0.012899160385131836 key: test_mcc value: [0.83953666 0.90267093 0.95998366 0.88420483 0.8304548 0.8304548 0.90267093 0.76200076 0.95959175 0.93990077] mean value: 0.8811469898781799 key: train_mcc value: [0.98645536 0.98198051 0.9887002 0.99546483 0.95771151 0.98421547 1. 0.96427411 0.9864704 0.99096035] mean value: 0.9836232730980496 key: test_fscore value: [0.92156863 0.95145631 0.98 0.94230769 0.91588785 0.91588785 0.95145631 0.88288288 0.97959184 0.97029703] mean value: 0.9411336391373023 key: train_fscore value: [0.99322799 0.99099099 0.99435028 0.99773243 0.97886541 0.99210823 1. 0.98214286 0.99324324 0.99547511] mean value: 0.9918136540255048 key: test_precision value: [0.88679245 0.90740741 0.96078431 0.89090909 0.84482759 0.84482759 0.90740741 0.79032258 0.96 0.94230769] mean value: 0.8935586117646231 key: train_precision value: [0.98654709 0.98214286 0.98876404 0.99547511 0.95860566 0.98434004 1. 0.96491228 0.98657718 0.99099099] mean value: 0.9838355262542189 key: test_recall value: [0.95918367 1. 1. 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9959183673469388 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.91836735 0.94897959 0.97959184 0.93877551 0.90816327 0.90816327 0.94897959 0.86734694 0.97938144 0.96907216] mean value: 0.9366820955186197 key: train_accuracy value: [0.99318182 0.99090909 0.99431818 0.99772727 0.97840909 0.99204545 1. 0.98181818 0.99318956 0.9954597 ] mean value: 0.9917058353111134 key: test_roc_auc value: [0.91836735 0.94897959 0.97959184 0.93877551 0.90816327 0.90816327 0.94897959 0.86734694 0.97959184 0.96875 ] mean value: 0.936670918367347 key: train_roc_auc value: [0.99318182 0.99090909 0.99431818 0.99772727 0.97840909 0.99204545 1. 0.98181818 0.99318182 0.99546485] mean value: 0.9917055761698619 key: test_jcc value: [0.85454545 0.90740741 0.96078431 0.89090909 0.84482759 0.84482759 0.90740741 0.79032258 0.96 0.94230769] mean value: 0.8903339119361497 key: train_jcc value: [0.98654709 0.98214286 0.98876404 0.99547511 0.95860566 0.98434004 1. 0.96491228 0.98657718 0.99099099] mean value: 0.9838355262542189 MCC on Blind test: 0.17 MCC on Training: 0.88 Running classifier: 14 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.01590037 0.01601338 0.01615906 0.01600718 0.01580024 0.02951574 0.01588249 0.01602983 0.0165143 0.01693964] mean value: 0.017476224899291994 key: score_time value: [0.01278591 0.01281524 0.01268935 0.01271033 0.01277113 0.01286817 0.01272321 0.01277423 0.01296997 0.01283646] mean value: 0.012794399261474609 key: test_mcc value: [0.32659863 0.42892887 0.57154761 0.39302868 0.55519838 0.45363235 0.42892887 0.22524154 0.4865312 0.23692048] mean value: 0.41065566222315564 key: train_mcc value: [0.420586 0.44574935 0.44778394 0.36928599 0.43904589 0.43866469 0.4281405 0.44552819 0.43479763 0.46035463] mean value: 0.43299368167961144 key: test_fscore value: [0.65979381 0.72 0.78350515 0.66666667 0.78846154 0.7032967 0.70833333 0.62745098 0.72527473 0.62626263] mean value: 0.7009045542759915 key: train_fscore value: [0.70655926 0.71759259 0.72164948 0.6713948 0.71312427 0.71771429 0.70422535 0.72018349 0.71542857 0.72196262] mean value: 0.7109834726120717 key: test_precision value: [0.66666667 0.70588235 0.79166667 0.73170732 0.74545455 0.76190476 0.72340426 0.60377358 0.76744186 0.62 ] mean value: 0.7117902011396914 key: train_precision value: [0.71561772 0.73113208 0.72748268 0.69950739 0.72921615 0.72183908 0.72815534 0.72685185 0.72119816 0.74278846] mean value: 0.7243788901592747 key: test_recall value: [0.65306122 0.73469388 0.7755102 0.6122449 0.83673469 0.65306122 0.69387755 0.65306122 0.6875 0.63265306] mean value: 0.6932397959183673 key: train_recall value: [0.69772727 0.70454545 0.71590909 0.64545455 0.69772727 0.71363636 0.68181818 0.71363636 0.70975057 0.70227273] mean value: 0.6982477839620697 key: test_accuracy value: [0.66326531 0.71428571 0.78571429 0.69387755 0.7755102 0.7244898 0.71428571 0.6122449 0.74226804 0.6185567 ] mean value: 0.7044498211655796 key: train_accuracy value: [0.71022727 0.72272727 0.72386364 0.68409091 0.71931818 0.71931818 0.71363636 0.72272727 0.71736663 0.72985244] mean value: 0.7163128160148591 key: test_roc_auc value: [0.66326531 0.71428571 0.78571429 0.69387755 0.7755102 0.7244898 0.71428571 0.6122449 0.74170918 0.61840986] mean value: 0.7043792517006802 key: train_roc_auc value: [0.71022727 0.72272727 0.72386364 0.68409091 0.71931818 0.71931818 0.71363636 0.72272727 0.71737528 0.72982117] mean value: 0.7163105545248403 key: test_jcc value: [0.49230769 0.5625 0.6440678 0.5 0.65079365 0.54237288 0.5483871 0.45714286 0.56896552 0.45588235] mean value: 0.5422419845167051 key: train_jcc value: [0.54626335 0.55956679 0.56451613 0.50533808 0.55415162 0.5597148 0.54347826 0.56272401 0.5569395 0.56489945] mean value: 0.5517591987620835 MCC on Blind test: 0.29 MCC on Training: 0.41 Running classifier: 15 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.01679969 0.01651716 0.01663876 0.01661992 0.0165782 0.01654887 0.01659799 0.01662302 0.0171237 0.01704931] mean value: 0.016709661483764647 key: score_time value: [0.01286197 0.01285362 0.01278877 0.01291203 0.01286817 0.01282477 0.01282454 0.01287317 0.01294208 0.01289821] mean value: 0.01286473274230957 key: test_mcc value: [0.1894422 0.27100983 0.41030497 0.36742346 0.41030497 0.34810057 0.40824829 0.04089304 0.32385876 0.23911405] mean value: 0.3008700134578962 key: train_mcc value: [0.3648698 0.37078026 0.37648362 0.37137742 0.3703924 0.36750808 0.38254586 0.42669906 0.36442937 0.38435398] mean value: 0.37794398593638034 key: test_fscore value: [0.53488372 0.59090909 0.68817204 0.68686869 0.68817204 0.65957447 0.70103093 0.50526316 0.62068966 0.60215054] mean value: 0.6277714331351232 key: train_fscore value: [0.64837905 0.66425121 0.66259169 0.6737338 0.66586538 0.66746126 0.66909091 0.68322981 0.66266507 0.66503067] mean value: 0.6662298863268711 key: test_precision value: [0.62162162 0.66666667 0.72727273 0.68 0.72727273 0.68888889 0.70833333 0.52173913 0.69230769 0.63636364] mean value: 0.6670466424162076 key: train_precision value: [0.71823204 0.70876289 0.71693122 0.6992665 0.70663265 0.70175439 0.71688312 0.75342466 0.70408163 0.72266667] mean value: 0.714863576415876 key: test_recall value: [0.46938776 0.53061224 0.65306122 0.69387755 0.65306122 0.63265306 0.69387755 0.48979592 0.5625 0.57142857] mean value: 0.5950255102040816 key: train_recall value: [0.59090909 0.625 0.61590909 0.65 0.62954545 0.63636364 0.62727273 0.625 0.62585034 0.61590909] mean value: 0.6241759431045145 key: test_accuracy value: [0.59183673 0.63265306 0.70408163 0.68367347 0.70408163 0.67346939 0.70408163 0.52040816 0.65979381 0.6185567 ] mean value: 0.6492636229749632 key: train_accuracy value: [0.67954545 0.68409091 0.68636364 0.68522727 0.68409091 0.68295455 0.68977273 0.71022727 0.68104427 0.69012486] mean value: 0.6873441853265916 key: test_roc_auc value: [0.59183673 0.63265306 0.70408163 0.68367347 0.70408163 0.67346939 0.70408163 0.52040816 0.65880102 0.61904762] mean value: 0.6492134353741497 key: train_roc_auc value: [0.67954545 0.68409091 0.68636364 0.68522727 0.68409091 0.68295455 0.68977273 0.71022727 0.68110699 0.69004071] mean value: 0.6873420428777571 key: test_jcc value: [0.36507937 0.41935484 0.52459016 0.52307692 0.52459016 0.49206349 0.53968254 0.33802817 0.45 0.43076923] mean value: 0.46072348862641654 key: train_jcc value: [0.4797048 0.49728752 0.49542962 0.5079929 0.4990991 0.50089445 0.50273224 0.51886792 0.49551167 0.49816176] mean value: 0.4995681983756118 MCC on Blind test: 0.15 MCC on Training: 0.3 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.02363181 0.02751517 0.03084803 0.02829027 0.02860403 0.02433419 0.02978444 0.02737546 0.03741193 0.02281022] mean value: 0.028060555458068848 key: score_time value: [0.01242781 0.01242042 0.01261568 0.01244521 0.01255322 0.01584816 0.01252055 0.01240039 0.01246262 0.01233387] mean value: 0.012802791595458985 key: test_mcc value: [0.59988588 0.40509575 0.44020439 0.14433757 0.58639547 0.3882736 0.3880785 0.26967994 0.28177677 0.31196037] mean value: 0.3815688244992497 key: train_mcc value: [0.61852189 0.45846559 0.50304579 0.15155541 0.58037358 0.50835237 0.59607064 0.36221021 0.32233598 0.4093197 ] mean value: 0.4510251151461596 key: test_fscore value: [0.81132075 0.74015748 0.752 0.07843137 0.80733945 0.57894737 0.7 0.70072993 0.25454545 0.54545455] mean value: 0.5968926352550599 key: train_fscore value: [0.82178218 0.75752212 0.77366997 0.10706638 0.80332986 0.63473054 0.79302326 0.72439634 0.34572491 0.57443609] mean value: 0.6335681649352007 key: test_precision value: [0.75438596 0.6025641 0.61842105 1. 0.73333333 0.81481481 0.68627451 0.54545455 1. 0.75 ] mean value: 0.7505248323514577 key: train_precision value: [0.72807018 0.62028986 0.64125561 0.92592593 0.74088292 0.92982456 0.81190476 0.57161629 0.95876289 0.84888889] mean value: 0.7777421872429201 key: test_recall value: [0.87755102 0.95918367 0.95918367 0.04081633 0.89795918 0.44897959 0.71428571 0.97959184 0.14583333 0.42857143] mean value: 0.6451955782312925 key: train_recall value: [0.94318182 0.97272727 0.975 0.05681818 0.87727273 0.48181818 0.775 0.98863636 0.21088435 0.43409091] mean value: 0.671542980828695 key: test_accuracy value: [0.79591837 0.66326531 0.68367347 0.52040816 0.78571429 0.67346939 0.69387755 0.58163265 0.57731959 0.63917526] mean value: 0.6614454029034293 key: train_accuracy value: [0.79545455 0.68863636 0.71477273 0.52613636 0.78522727 0.72272727 0.79772727 0.62386364 0.60045403 0.67877412] mean value: 0.6933773604375193 key: test_roc_auc value: [0.79591837 0.66326531 0.68367347 0.52040816 0.78571429 0.67346939 0.69387755 0.58163265 0.57291667 0.64136905] mean value: 0.6612244897959184 key: train_roc_auc value: [0.79545455 0.68863636 0.71477273 0.52613636 0.78522727 0.72272727 0.79772727 0.62386364 0.60089672 0.6784967 ] mean value: 0.6933938878581736 key: test_jcc value: [0.68253968 0.5875 0.6025641 0.04081633 0.67692308 0.40740741 0.53846154 0.53932584 0.14583333 0.375 ] mean value: 0.4596371310456383 key: train_jcc value: [0.69747899 0.60968661 0.63088235 0.05656109 0.67130435 0.46491228 0.65703276 0.56788512 0.20898876 0.40295359] mean value: 0.4967685892060075 MCC on Blind test: 0.22 MCC on Training: 0.38 Running classifier: 17 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.04042196 0.04128051 0.03928638 0.04059577 0.04120302 0.03969407 0.04082251 0.04092097 0.04168749 0.04225564] mean value: 0.04081683158874512 key: score_time value: [0.01317334 0.01330042 0.01322174 0.01328421 0.01315975 0.01314044 0.01330161 0.01327395 0.01326919 0.01325703] mean value: 0.013238167762756348 key: test_mcc value: [0.95998366 1. 1. 1. 1. 1. 0.9797959 0.9797959 1. 0.97958324] mean value: 0.9899158698936755 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.97916667 1. 1. 1. 1. 1. 0.98989899 0.98989899 1. 0.98989899] mean value: 0.9948863636363636 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 1. 1. 1. 1. 0.98 0.98 1. 0.98] mean value: 0.9940000000000001 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.95918367 1. 1. 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9959183673469388 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.97959184 1. 1. 1. 1. 1. 0.98979592 0.98979592 1. 0.98969072] mean value: 0.9948874395118873 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.97959184 1. 1. 1. 1. 1. 0.98979592 0.98979592 1. 0.98958333] mean value: 0.9948767006802722 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.95918367 1. 1. 1. 1. 1. 0.98 0.98 1. 0.98 ] mean value: 0.9899183673469387 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.02 MCC on Training: 0.99 Running classifier: 18 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42))]) key: fit_time value: [0.8119061 0.79044056 0.81919289 0.78098702 0.80824161 0.80549049 0.82822394 0.76991057 0.77431011 0.79413033] mean value: 0.7982833623886109 key: score_time value: [0.19262886 0.18665195 0.16593409 0.14881611 0.166224 0.21661353 0.17060924 0.17478895 0.12652588 0.18169975] mean value: 0.1730492353439331 key: test_mcc value: [0.93897107 0.95998366 0.95998366 1. 0.9797959 0.95998366 0.9797959 0.9797959 0.97959184 0.92058909] mean value: 0.9658490668344578 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.96907216 0.98 0.98 1. 0.98989899 0.98 0.98989899 0.98989899 0.98969072 0.96078431] mean value: 0.9829244170020399 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.97916667 0.96078431 0.96078431 1. 0.98 0.96078431 0.98 0.98 0.97959184 0.9245283 ] mean value: 0.9705639746464623 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.95918367 1. 1. 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9959183673469388 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.96938776 0.97959184 0.97959184 1. 0.98979592 0.97959184 0.98979592 0.98979592 0.98969072 0.95876289] mean value: 0.9826004628655586 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.96938776 0.97959184 0.97959184 1. 0.98979592 0.97959184 0.98979592 0.98979592 0.98979592 0.95833333] mean value: 0.9825680272108844 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.94 0.96078431 0.96078431 1. 0.98 0.96078431 0.98 0.98 0.97959184 0.9245283 ] mean value: 0.9666473079797957 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.23 MCC on Training: 0.97 Running classifier: 19 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea...age_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42))]) key: fit_time value: [1.23564529 1.18444419 1.18077993 1.1737237 1.14174509 1.20027709 1.1202929 1.16322327 1.21920133 1.10419464] mean value: 1.1723527431488037 key: score_time value: [0.27239704 0.24183846 0.23902273 0.29935932 0.26668644 0.23746872 0.2401135 0.25685024 0.17717886 0.258389 ] mean value: 0.24893043041229249 key: test_mcc value: [0.85732141 0.8996469 0.87828292 0.95998366 0.8996469 0.89814624 0.85732141 0.92144268 0.95959175 0.9016018 ] mean value: 0.9032985669524942 key: train_mcc value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( [0.99091933 0.98411378 0.98411378 0.9932049 0.98637383 0.98411378 0.98181818 0.98421547 0.99096016 0.99321263] mean value: 0.9873045857765778 key: test_fscore value: [0.92783505 0.95049505 0.94 0.98 0.95049505 0.94949495 0.92783505 0.96078431 0.97959184 0.95145631] mean value: 0.9517987612737431 key: train_fscore value: [0.99546485 0.99207248 0.99207248 0.99660249 0.99319728 0.99207248 0.99090909 0.99210823 0.99548533 0.99660249] mean value: 0.9936587203286921 key: test_precision value: [0.9375 0.92307692 0.92156863 0.96078431 0.92307692 0.94 0.9375 0.9245283 0.96 0.90740741] mean value: 0.9335442496624516 key: train_precision value: [0.99321267 0.98871332 0.98871332 0.99322799 0.99095023 0.98871332 0.99090909 0.98434004 0.99101124 0.99322799] mean value: 0.9903019204329061 key: test_recall value: [0.91836735 0.97959184 0.95918367 1. 0.97959184 0.95918367 0.91836735 1. 1. 1. ] mean value: 0.9714285714285713 key: train_recall value: [0.99772727 0.99545455 0.99545455 1. 0.99545455 0.99545455 0.99090909 1. 1. 1. ] mean value: 0.9970454545454546 key: test_accuracy value: [0.92857143 0.94897959 0.93877551 0.97959184 0.94897959 0.94897959 0.92857143 0.95918367 0.97938144 0.94845361] mean value: 0.9509467704607616 key: train_accuracy value: [0.99545455 0.99204545 0.99204545 0.99659091 0.99318182 0.99204545 0.99090909 0.99204545 0.9954597 0.99659478] mean value: 0.9936372665359613 key: test_roc_auc value: [0.92857143 0.94897959 0.93877551 0.97959184 0.94897959 0.94897959 0.92857143 0.95918367 0.97959184 0.94791667] mean value: 0.9509141156462583 key: train_roc_auc value: [0.99545455 0.99204545 0.99204545 0.99659091 0.99318182 0.99204545 0.99090909 0.99204545 0.99545455 0.99659864] mean value: 0.9936371366728511 key: test_jcc value: [0.86538462 0.90566038 0.88679245 0.96078431 0.90566038 0.90384615 0.86538462 0.9245283 0.96 0.90740741] mean value: 0.9085448615182244 key: train_jcc value: [0.99097065 0.98426966 0.98426966 0.99322799 0.98648649 0.98426966 0.98198198 0.98434004 0.99101124 0.99322799] mean value: 0.9874055374499147 MCC on Blind test: 0.41 MCC on Training: 0.9 Running classifier: 20 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.03962827 0.04709029 0.04175019 0.05819559 0.05068731 0.04236984 0.04261994 0.04730678 0.03896904 0.03761387] mean value: 0.04462311267852783 key: score_time value: [0.01873827 0.02903509 0.01935554 0.01901221 0.01920915 0.01900268 0.01917553 0.01917338 0.02898455 0.02995825] mean value: 0.022164463996887207 key: test_mcc value: [0.55102041 0.63265306 0.46977924 0.51020408 0.65648795 0.53339646 0.5119126 0.30714756 0.69076995 0.55317564] mean value: 0.5416546945819849 key: train_mcc value: [0.66599338 0.66407557 0.66174829 0.66211818 0.65067253 0.66140634 0.65460632 0.69322658 0.64154324 0.68253517] mean value: 0.6637925600202131 key: test_fscore value: [0.7755102 0.81632653 0.74 0.75510204 0.83495146 0.77669903 0.76470588 0.66666667 0.84210526 0.79245283] mean value: 0.776451990331328 key: train_fscore value: [0.83427283 0.83482143 0.83351955 0.83462819 0.82888889 0.83163842 0.82844244 0.84745763 0.82326622 0.84340045] mean value: 0.8340336040982848 key: test_precision value: [0.7755102 0.81632653 0.7254902 0.75510204 0.7962963 0.74074074 0.73584906 0.64150943 0.85106383 0.73684211] mean value: 0.7574730434242103 key: train_precision value: [0.82774049 0.82017544 0.81978022 0.81561822 0.81086957 0.82696629 0.82286996 0.84269663 0.81236203 0.83039648] mean value: 0.8229475320203526 key: test_recall value: [0.7755102 0.81632653 0.75510204 0.75510204 0.87755102 0.81632653 0.79591837 0.69387755 0.83333333 0.85714286] mean value: 0.7976190476190476 key: train_recall value: [0.84090909 0.85 0.84772727 0.85454545 0.84772727 0.83636364 0.83409091 0.85227273 0.83446712 0.85681818] mean value: 0.8454921665635953 key: test_accuracy value: [0.7755102 0.81632653 0.73469388 0.75510204 0.82653061 0.76530612 0.75510204 0.65306122 0.84536082 0.77319588] mean value: 0.7700189354092152 key: train_accuracy value: [0.83295455 0.83181818 0.83068182 0.83068182 0.825 0.83068182 0.82727273 0.84659091 0.82065834 0.84108967] mean value: 0.8317429831802704 key: test_roc_auc value: [0.7755102 0.81632653 0.73469388 0.75510204 0.82653061 0.76530612 0.75510204 0.65306122 0.8452381 0.77232143] mean value: 0.7699192176870749 key: train_roc_auc value: [0.83295455 0.83181818 0.83068182 0.83068182 0.825 0.83068182 0.82727273 0.84659091 0.82064265 0.8411075 ] mean value: 0.8317431972789116 key: test_jcc value: [0.63333333 0.68965517 0.58730159 0.60655738 0.71666667 0.63492063 0.61904762 0.5 0.72727273 0.65625 ] mean value: 0.6371005118005543 key: train_jcc value: [0.71566731 0.7164751 0.71455939 0.71619048 0.70777989 0.71179884 0.70712909 0.73529412 0.69961977 0.72920696] mean value: 0.7153720943139535 MCC on Blind test: 0.29 MCC on Training: 0.54 Running classifier: 21 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=3))]) key: fit_time value: [0.1302042 0.14666224 0.16604614 0.13338852 0.13227105 0.08911824 0.11750293 0.13315201 0.0922935 0.13435245] mean value: 0.12749912738800048 key: score_time value: [0.01915646 0.01929879 0.01879907 0.01895571 0.01230311 0.01221395 0.01908779 0.01909184 0.01882458 0.01961517] mean value: 0.017734646797180176 key: test_mcc value: [0.53160953 0.57250257 0.55147997 0.53611096 0.70106818 0.55519838 0.49827288 0.30844355 0.71179283 0.5574084 ] mean value: 0.5523887266180604 key: train_mcc value: [0.69563423 0.6963255 0.70340905 0.70359868 0.69924769 0.71251467 0.73376128 0.73022659 0.69462968 0.66419755] mean value: 0.7033544923646413 key: test_fscore value: [0.77227723 0.79207921 0.78 0.78095238 0.85714286 0.78846154 0.76635514 0.67307692 0.85106383 0.7962963 ] mean value: 0.785770540154771 key: train_fscore value: [0.8494382 0.85144124 0.85524862 0.85556781 0.85368537 0.85966851 0.87061404 0.8676307 0.85115766 0.83370787] mean value: 0.8548160009153642 key: test_precision value: [0.75 0.76923077 0.76470588 0.73214286 0.80357143 0.74545455 0.70689655 0.63636364 0.86956522 0.72881356] mean value: 0.7506744447553654 key: train_precision value: [0.84 0.83116883 0.83225806 0.83083512 0.82729211 0.83655914 0.84110169 0.8496732 0.82832618 0.82444444] mean value: 0.8341658786348715 key: test_recall value: [0.79591837 0.81632653 0.79591837 0.83673469 0.91836735 0.83673469 0.83673469 0.71428571 0.83333333 0.87755102] mean value: 0.8261904761904763 key: train_recall value: [0.85909091 0.87272727 0.87954545 0.88181818 0.88181818 0.88409091 0.90227273 0.88636364 0.87528345 0.84318182] mean value: 0.8766192537621109 key: test_accuracy value: [0.76530612 0.78571429 0.7755102 0.76530612 0.84693878 0.7755102 0.74489796 0.65306122 0.8556701 0.77319588] mean value: 0.7741110877340627 key: train_accuracy value: [0.84772727 0.84772727 0.85113636 0.85113636 0.84886364 0.85568182 0.86590909 0.86477273 0.84676504 0.83200908] mean value: 0.8511728665772367 key: test_roc_auc value: [0.76530612 0.78571429 0.7755102 0.76530612 0.84693878 0.7755102 0.74489796 0.65306122 0.85544218 0.77210884] mean value: 0.7739795918367347 key: train_roc_auc value: [0.84772727 0.84772727 0.85113636 0.85113636 0.84886364 0.85568182 0.86590909 0.86477273 0.84673263 0.83202175] mean value: 0.851170892599464 key: test_jcc value: [0.62903226 0.6557377 0.63934426 0.640625 0.75 0.65079365 0.62121212 0.50724638 0.74074074 0.66153846] mean value: 0.6496270576374199 key: train_jcc value: [0.73828125 0.74131274 0.74710425 0.74759152 0.74472169 0.75387597 0.77087379 0.76620825 0.74088292 0.71483622] mean value: 0.7465688597481133 MCC on Blind test: 0.23 MCC on Training: 0.55 Running classifier: 22 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.06782627 0.04741096 0.04699683 0.0470016 0.04986525 0.05053234 0.04853296 0.04638076 0.04855275 0.05508161] mean value: 0.050818133354187014 key: score_time value: [0.02016783 0.01952362 0.01970792 0.01964045 0.02014565 0.02007151 0.01966357 0.01916218 0.01955056 0.02212644] mean value: 0.019975972175598145 key: test_mcc value: [0.5119126 0.65648795 0.63477162 0.63477162 0.67346939 0.57442696 0.57250257 0.32659863 0.67208128 0.62920443] mean value: 0.588622704856949 key: train_mcc value: [0.72008871 0.71632547 0.69211849 0.68008884 0.71158604 0.69649142 0.6847296 0.73257537 0.69433565 0.72254092] mean value: 0.7050880510705836 key: test_fscore value: [0.74468085 0.8172043 0.80851064 0.80851064 0.83673469 0.77419355 0.77894737 0.65979381 0.82608696 0.82 ] mean value: 0.7874662810375272 key: train_fscore value: [0.85377358 0.85549133 0.84074941 0.83095238 0.85385501 0.84345794 0.83855981 0.8627907 0.84320557 0.85512367] mean value: 0.8477959421205231 key: test_precision value: [0.77777778 0.86363636 0.84444444 0.84444444 0.83673469 0.81818182 0.80434783 0.66666667 0.86363636 0.80392157] mean value: 0.8123791967379838 key: train_precision value: [0.8872549 0.87058824 0.86714976 0.8725 0.86480186 0.86778846 0.85748219 0.88333333 0.86428571 0.88753056] mean value: 0.8722715017288728 key: test_recall value: [0.71428571 0.7755102 0.7755102 0.7755102 0.83673469 0.73469388 0.75510204 0.65306122 0.79166667 0.83673469] mean value: 0.7648809523809524 key: train_recall value: [0.82272727 0.84090909 0.81590909 0.79318182 0.84318182 0.82045455 0.82045455 0.84318182 0.82312925 0.825 ] mean value: 0.824812925170068 key: test_accuracy value: [0.75510204 0.82653061 0.81632653 0.81632653 0.83673469 0.78571429 0.78571429 0.66326531 0.83505155 0.81443299] mean value: 0.7935198821796761 key: train_accuracy value: [0.85909091 0.85795455 0.84545455 0.83863636 0.85568182 0.84772727 0.84204545 0.86590909 0.84676504 0.86038593] mean value: 0.8519650964812714 key: test_roc_auc value: [0.75510204 0.82653061 0.81632653 0.81632653 0.83673469 0.78571429 0.78571429 0.66326531 0.83460884 0.81420068] mean value: 0.793452380952381 key: train_roc_auc value: [0.85909091 0.85795455 0.84545455 0.83863636 0.85568182 0.84772727 0.84204545 0.86590909 0.8467919 0.8603458 ] mean value: 0.8519637703566275 key: test_jcc value: [0.59322034 0.69090909 0.67857143 0.67857143 0.71929825 0.63157895 0.63793103 0.49230769 0.7037037 0.69491525] mean value: 0.6521007164748899 key: train_jcc value: [0.74485597 0.74747475 0.72525253 0.7107943 0.74497992 0.72929293 0.722 0.75869121 0.72891566 0.74691358] mean value: 0.7359170835570932 MCC on Blind test: 0.32 MCC on Training: 0.59 Running classifier: 23 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.0233252 0.04018211 0.03728056 0.04791975 0.03962517 0.03027844 0.03462052 0.03575158 0.03992558 0.03843331] mean value: 0.03673422336578369 key: score_time value: [0.01004386 0.01174998 0.01205397 0.01205111 0.0120697 0.01226163 0.01215124 0.01209474 0.01209593 0.01202011] mean value: 0.01185922622680664 key: test_mcc value: [0.43778511 0.47095959 0.52223297 0.58297525 0.65252314 0.49071649 0.31799936 0.38651034 0.65581352 0.44957495] mean value: 0.49670907198281977 key: train_mcc value: [0.59690889 0.65756754 0.64447226 0.60772529 0.67219751 0.61603798 0.57637198 0.58328238 0.62268715 0.52379486] mean value: 0.6101045834207636 key: test_fscore value: [0.68181818 0.72340426 0.7826087 0.80701754 0.83636364 0.73684211 0.71014493 0.73282443 0.80898876 0.65853659] mean value: 0.7478549122703894 key: train_fscore value: [0.77571252 0.8242142 0.83283283 0.81509434 0.84421053 0.80597015 0.80147738 0.80406654 0.79807692 0.6779661 ] mean value: 0.7979621511940159 key: test_precision value: [0.76923077 0.75555556 0.68181818 0.70769231 0.75409836 0.76086957 0.5505618 0.58536585 0.87804878 0.81818182] mean value: 0.7261422990250912 key: train_precision value: [0.85286104 0.84486874 0.74418605 0.69677419 0.78627451 0.81438515 0.67496112 0.67757009 0.84910486 0.89552239] mean value: 0.7836508131785728 key: test_recall value: [0.6122449 0.69387755 0.91836735 0.93877551 0.93877551 0.71428571 1. 0.97959184 0.75 0.55102041] /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:463: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_CV['source_data'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:490: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_BT['source_data'] = 'BT' mean value: 0.8096938775510203 key: train_recall value: [0.71136364 0.80454545 0.94545455 0.98181818 0.91136364 0.79772727 0.98636364 0.98863636 0.75283447 0.54545455] mean value: 0.8425561739847455 key: test_accuracy value: [0.71428571 0.73469388 0.74489796 0.7755102 0.81632653 0.74489796 0.59183673 0.64285714 0.82474227 0.71134021] mean value: 0.7301388596675784 key: train_accuracy value: [0.79431818 0.82840909 0.81022727 0.77727273 0.83181818 0.80795455 0.75568182 0.75909091 0.8093076 0.74120318] mean value: 0.7915283510473634 key: test_roc_auc value: [0.71428571 0.73469388 0.74489796 0.7755102 0.81632653 0.74489796 0.59183673 0.64285714 0.82397959 0.7130102 ] mean value: 0.7302295918367347 key: train_roc_auc value: [0.79431818 0.82840909 0.81022727 0.77727273 0.83181818 0.80795455 0.75568182 0.75909091 0.80937178 0.74098124] mean value: 0.7915125747268605 key: test_jcc value: [0.51724138 0.56666667 0.64285714 0.67647059 0.71875 0.58333333 0.5505618 0.57831325 0.67924528 0.49090909] mean value: 0.6004348535095598 key: train_jcc value: [0.63360324 0.7009901 0.7135506 0.68789809 0.73041894 0.675 0.66872111 0.67233385 0.664 0.51282051] mean value: 0.6659336441676296 MCC on Blind test: 0.18 MCC on Training: 0.5 Running classifier: 24 Model_name: XGBoost Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.1785326 0.15418243 0.13813758 0.16065526 0.30703521 0.13473225 0.13479948 0.14042187 0.16910315 0.1515305 ] mean value: 0.16691303253173828 key: score_time value: [0.0114255 0.01224947 0.01154566 0.01259446 0.01200032 0.01199675 0.01142859 0.01162457 0.01160598 0.01244593] mean value: 0.011891722679138184 key: test_mcc value: [0.87828292 0.94053994 0.92144268 1. 0.94053994 0.92144268 0.95998366 0.9797959 0.97959184 0.92058909] mean value: 0.94422086457927 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.94 0.97029703 0.96078431 1. 0.97029703 0.96078431 0.98 0.98989899 0.98969072 0.96078431] mean value: 0.9722536712130886 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.92156863 0.94230769 0.9245283 1. 0.94230769 0.9245283 0.96078431 0.98 0.97959184 0.9245283 ] mean value: 0.9500145068186925 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.95918367 1. 1. 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9959183673469388 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.93877551 0.96938776 0.95918367 1. 0.96938776 0.95918367 0.97959184 0.98979592 0.98969072 0.95876289] mean value: 0.9713759730696403 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.93877551 0.96938776 0.95918367 1. 0.96938776 0.95918367 0.97959184 0.98979592 0.98979592 0.95833333] mean value: 0.9713435374149662 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.88679245 0.94230769 0.9245283 1. 0.94230769 0.9245283 0.96078431 0.98 0.97959184 0.9245283 ] mean value: 0.9465368893566133 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.41 MCC on Training: 0.94 Extracting tts_split_name: 70_30 Total cols in each df: CV df: 8 metaDF: 17 Adding column: Model_name Total cols in bts df: BT_df: 8 First proceeding to rowbind CV and BT dfs: Final output should have: 25 columns Combinig 2 using pd.concat by row ~ rowbind Checking Dims of df to combine: Dim of CV: (24, 8) Dim of BT: (24, 8) 8 Number of Common columns: 8 These are: ['source_data', 'Precision', 'JCC', 'MCC', 'Recall', 'Accuracy', 'F1', 'ROC_AUC'] Concatenating dfs with different resampling methods [WF]: Split type: 70_30 No. of dfs combining: 2 PASS: 2 dfs successfully combined nrows in combined_df_wf: 48 ncols in combined_df_wf: 8 PASS: proceeding to merge metadata with CV and BT dfs Adding column: Model_name ========================================================= SUCCESS: Ran multiple classifiers ======================================================= ============================================================== Running several classification models (n): 24 List of models: ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('Gaussian NB', GaussianNB()) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('K-Nearest Neighbors', KNeighborsClassifier()) ('LDA', LinearDiscriminantAnalysis()) ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(cv=3, random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Multinomial', MultinomialNB()) ('Naive Bayes', BernoulliNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=12, random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Random Forest', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42)) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=3)) ('SVC', SVC(random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=12, random_state=42)) [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... 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[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... 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Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Œ loky_p¡àƒçUpŒ“þ~[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.5s remaining: 1.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.5s remaining: 1.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.5s remaining: 1.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.5s remaining: 1.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.5s remaining: 0.2s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.5s remaining: 1.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.5s remaining: 1.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.5s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.6s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.5s remaining: 0.2s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.6s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.6s remaining: 1.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.6s remaining: 1.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.6s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.6s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.6s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.6s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.6s remaining: 0.2s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.6s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.6s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.6s remaining: 1.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.6s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.6s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.6s remaining: 1.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.6s remaining: 0.2s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.6s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.6s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.6s remaining: 0.2s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.6s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.6s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ================================================================ Running classifier: 1 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.11080766 0.10556388 0.11282945 0.10678983 0.1098597 0.11352944 0.11670256 0.11768556 0.11453938 0.11373186] mean value: 0.11220393180847169 key: score_time value: [0.01537991 0.01600933 0.01471543 0.01649165 0.01715398 0.01665568 0.01768351 0.01773548 0.01634693 0.01784444] mean value: 0.01660163402557373 key: test_mcc value: [0.41666667 0.60858062 0.44970061 0.41666667 0.52777778 0.65277778 0.18055556 0.07042952 0.54935027 0.34993386] mean value: 0.42224393180982833 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.70588235 0.8 0.73684211 0.70588235 0.75 0.82352941 0.58823529 0.5 0.75 0.57142857] mean value: 0.6931800088456435 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.66666667 0.66666667 0.63636364 0.66666667 0.75 0.875 0.625 0.57142857 0.85714286 0.8 ] mean value: 0.7114935064935064 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.75 1. 0.875 0.75 0.75 0.77777778 0.55555556 0.44444444 0.66666667 0.44444444] mean value: 0.701388888888889 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.70588235 0.76470588 0.70588235 0.70588235 0.76470588 0.82352941 0.58823529 0.52941176 0.76470588 0.64705882] mean value: 0.7000000000000001 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.70833333 0.77777778 0.71527778 0.70833333 0.76388889 0.82638889 0.59027778 0.53472222 0.77083333 0.65972222] mean value: 0.7055555555555555 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.54545455 0.66666667 0.58333333 0.54545455 0.6 0.7 0.41666667 0.33333333 0.6 0.4 ] mean value: 0.5390909090909091 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.14 MCC on Training: 0.42 Running classifier: 2 Model_name: Bagging Classifier Model func: BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3))]) key: fit_time value: [0.15713882 0.17230153 0.16332769 0.15847564 0.15261102 0.16163683 0.16224241 0.17048907 0.18701649 0.1674552 ] mean value: 0.16526947021484376 key: score_time value: [0.07049489 0.03793478 0.07753634 0.04175043 0.04061174 0.03759432 0.05019498 0.03832459 0.07050157 0.06079626] mean value: 0.052573990821838376 key: test_mcc value: [0.6846532 0.54935027 0.54935027 0.30988989 0.65277778 0.44970061 0.6479516 0.18055556 0.30988989 0.34993386] mean value: 0.4684052910857379 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.76923077 0.77777778 0.77777778 0.66666667 0.82352941 0.66666667 0.84210526 0.58823529 0.625 0.57142857] mean value: 0.7108418198588476 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.7 0.7 0.6 0.77777778 0.83333333 0.8 0.625 0.71428571 0.8 ] mean value: 0.7550396825396826 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.625 0.875 0.875 0.75 0.875 0.55555556 0.88888889 0.55555556 0.55555556 0.44444444] mean value: 0.7 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.82352941 0.76470588 0.76470588 0.64705882 0.82352941 0.70588235 0.82352941 0.58823529 0.64705882 0.64705882] mean value: 0.723529411764706 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.8125 0.77083333 0.77083333 0.65277778 0.82638889 0.71527778 0.81944444 0.59027778 0.65277778 0.65972222] mean value: 0.7270833333333334 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.625 0.63636364 0.63636364 0.5 0.7 0.5 0.72727273 0.41666667 0.45454545 0.4 ] mean value: 0.5596212121212121 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.19 MCC on Training: 0.47 Running classifier: 3 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.01404476 0.01562452 0.01447177 0.01502538 0.01477432 0.01472688 0.01547933 0.01503253 0.01476288 0.01430178] mean value: 0.014824414253234863 key: score_time value: [0.00887084 0.00915885 0.0094974 0.00904894 0.00901341 0.00922275 0.00909805 0.00990629 0.0091517 0.00967956] mean value: 0.009264779090881348 key: test_mcc value: [0.52777778 0.65277778 0.30988989 0.41666667 0.29166667 0.44970061 0.29166667 0.18055556 0.04351941 0.34993386] mean value: 0.3514154885903234 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.75 0.82352941 0.66666667 0.70588235 0.625 0.66666667 0.66666667 0.58823529 0.6 0.57142857] mean value: 0.6664075630252101 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.75 0.77777778 0.6 0.66666667 0.625 0.83333333 0.66666667 0.625 0.54545455 0.8 ] mean value: 0.6889898989898989 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.75 0.875 0.75 0.75 0.625 0.55555556 0.66666667 0.55555556 0.66666667 0.44444444] mean value: 0.663888888888889 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.76470588 0.82352941 0.64705882 0.70588235 0.64705882 0.70588235 0.64705882 0.58823529 0.52941176 0.64705882] mean value: 0.6705882352941177 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.76388889 0.82638889 0.65277778 0.70833333 0.64583333 0.71527778 0.64583333 0.59027778 0.52083333 0.65972222] mean value: 0.6729166666666666 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.6 0.7 0.5 0.54545455 0.45454545 0.5 0.5 0.41666667 0.42857143 0.4 ] mean value: 0.5045238095238096 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.26 MCC on Training: 0.35 Running classifier: 4 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.00860071 0.00981832 0.01010489 0.01015139 0.01004338 0.00988626 0.00998974 0.01003575 0.01018715 0.01012897] mean value: 0.009894657135009765 key: score_time value: [0.01044369 0.00944519 0.00939322 0.00914717 0.00933504 0.00963116 0.00949955 0.00948715 0.00925684 0.00857234] mean value: 0.009421133995056152 key: test_mcc value: [ 0.30988989 -0.18055556 0.07042952 0.29166667 0.2030906 0.24514517 0.6479516 0.05555556 0.44970061 -0.16903085] mean value: 0.19238432043972556 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.66666667 0.375 0.55555556 0.625 0.63157895 0.46153846 0.84210526 0.55555556 0.66666667 0.375 ] mean value: 0.5754667116509222 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.6 0.375 0.5 0.625 0.54545455 0.75 0.8 0.55555556 0.83333333 0.42857143] mean value: 0.6012914862914862 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.75 0.375 0.625 0.625 0.75 0.33333333 0.88888889 0.55555556 0.55555556 0.33333333] mean value: 0.5791666666666666 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.64705882 0.41176471 0.52941176 0.64705882 0.58823529 0.58823529 0.82352941 0.52941176 0.70588235 0.41176471] mean value: 0.5882352941176471 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.65277778 0.40972222 0.53472222 0.64583333 0.59722222 0.60416667 0.81944444 0.52777778 0.71527778 0.41666667] mean value: 0.5923611111111111 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.5 0.23076923 0.38461538 0.45454545 0.46153846 0.3 0.72727273 0.38461538 0.5 0.23076923] mean value: 0.4174125874125874 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.04 MCC on Training: 0.19 Running classifier: 5 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.09924126 0.09844232 0.1003294 0.10292602 0.09790587 0.10335922 0.09870505 0.10345578 0.10006976 0.0939827 ] mean value: 0.09984173774719238 key: score_time value: [0.01879501 0.0191164 0.01895213 0.01900864 0.0188334 0.01896381 0.01922607 0.01856756 0.01760769 0.01888561] mean value: 0.01879563331604004 key: test_mcc value: [0.42600643 0.43643578 0.65277778 0.24514517 0.69631062 0.44970061 0.41666667 0.44970061 0.30988989 0.34993386] mean value: 0.4432567423299164 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.61538462 0.72727273 0.82352941 0.66666667 0.84210526 0.66666667 0.70588235 0.66666667 0.625 0.57142857] mean value: 0.6910602941949691 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.8 0.57142857 0.77777778 0.53846154 0.72727273 0.83333333 0.75 0.83333333 0.71428571 0.8 ] mean value: 0.7345892995892995 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 1. 0.875 0.875 1. 0.55555556 0.66666667 0.55555556 0.55555556 0.44444444] mean value: 0.7027777777777777 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.70588235 0.64705882 0.82352941 0.58823529 0.82352941 0.70588235 0.70588235 0.70588235 0.64705882 0.64705882] mean value: 0.7000000000000001 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.69444444 0.66666667 0.82638889 0.60416667 0.83333333 0.71527778 0.70833333 0.71527778 0.65277778 0.65972222] mean value: 0.7076388888888889 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.44444444 0.57142857 0.7 0.5 0.72727273 0.5 0.54545455 0.5 0.45454545 0.4 ] mean value: 0.5343145743145743 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.21 MCC on Training: 0.44 Running classifier: 6 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.29003716 0.29404593 0.30052328 0.30366731 0.30998659 0.29877377 0.30082607 0.30944848 0.29666615 0.29708338] mean value: 0.3001058101654053 key: score_time value: [0.00896955 0.00925541 0.01037335 0.01004982 0.0097928 0.00946569 0.01023793 0.01000404 0.00917912 0.00996685] mean value: 0.009729456901550294 key: test_mcc value: [0.52777778 0.44970061 0.44970061 0.41666667 0.76388889 0.44970061 0.40849122 0.18055556 0.18055556 0.52297636] mean value: 0.43500138615883766 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.75 0.73684211 0.73684211 0.70588235 0.875 0.66666667 0.73684211 0.58823529 0.58823529 0.61538462] mean value: 0.6999930539017225 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.75 0.63636364 0.63636364 0.66666667 0.875 0.83333333 0.7 0.625 0.625 1. ] mean value: 0.7347727272727272 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.75 0.875 0.875 0.75 0.875 0.55555556 0.77777778 0.55555556 0.55555556 0.44444444] mean value: 0.701388888888889 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.76470588 0.70588235 0.70588235 0.70588235 0.88235294 0.70588235 0.70588235 0.58823529 0.58823529 0.70588235] mean value: 0.7058823529411765 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.76388889 0.71527778 0.71527778 0.70833333 0.88194444 0.71527778 0.70138889 0.59027778 0.59027778 0.72222222] mean value: 0.7104166666666667 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.6 0.58333333 0.58333333 0.54545455 0.77777778 0.5 0.58333333 0.41666667 0.41666667 0.44444444] mean value: 0.5451010101010102 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.24 MCC on Training: 0.44 Running classifier: 7 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.0090344 0.00970435 0.01027465 0.00981212 0.00915122 0.00907063 0.00861835 0.00928688 0.00958991 0.01002789] mean value: 0.009457039833068847 key: score_time value: [0.00859046 0.00923419 0.00950241 0.00929928 0.00853157 0.00877309 0.00931406 0.00879216 0.00897217 0.00989342] mean value: 0.009090280532836914 key: test_mcc value: [-0.09128709 0.40849122 0.41666667 0.31051721 0.38729833 0.52297636 0.52297636 0.24514517 0.43643578 0.52297636] mean value: 0.3682196375957642 key: train_mcc value: [0.55253276 0.44662541 0.46815387 0.47876731 0.50994948 0.46417827 0.46417827 0.46417827 0.47514125 0.4854898 ] mean value: 0.4809194684015351 key: test_fscore value: [0.30769231 0.66666667 0.70588235 0.5 0.4 0.61538462 0.61538462 0.46153846 0.5 0.61538462] mean value: 0.5387933634992458 key: train_fscore value: [0.67226891 0.55045872 0.57657658 0.56880734 0.60714286 0.54716981 0.54716981 0.54716981 0.58181818 0.57407407] mean value: 0.577265608618285 key: test_precision value: [0.4 0.71428571 0.66666667 0.75 1. 1. 1. 0.75 1. 1. ] mean value: 0.8280952380952382 key: train_precision value: [0.95238095 0.9375 0.94117647 0.96875 0.97142857 0.96666667 0.96666667 0.96666667 0.94117647 0.96875 ] mean value: 0.9581162464985994 key: test_recall value: [0.25 0.625 0.75 0.375 0.25 0.44444444 0.44444444 0.33333333 0.33333333 0.44444444] mean value: 0.425 key: train_recall value: [0.51948052 0.38961039 0.41558442 0.4025974 0.44155844 0.38157895 0.38157895 0.38157895 0.42105263 0.40789474] mean value: 0.4142515379357484 key: test_accuracy value: [0.47058824 0.70588235 0.70588235 0.64705882 0.64705882 0.70588235 0.70588235 0.58823529 0.64705882 0.70588235] mean value: 0.6529411764705884 key: train_accuracy value: [0.74509804 0.67973856 0.69281046 0.69281046 0.7124183 0.68627451 0.68627451 0.68627451 0.69934641 0.69934641] mean value: 0.6980392156862745 key: test_roc_auc value: [0.45833333 0.70138889 0.70833333 0.63194444 0.625 0.72222222 0.72222222 0.60416667 0.66666667 0.72222222] mean value: 0.65625 key: train_roc_auc value: [0.74658237 0.6816473 0.69463431 0.69471975 0.71420027 0.68429597 0.68429597 0.68429597 0.6975393 0.69745386] mean value: 0.6979665071770335 key: test_jcc value: [0.18181818 0.5 0.54545455 0.33333333 0.25 0.44444444 0.44444444 0.3 0.33333333 0.44444444] mean value: 0.37772727272727274 key: train_jcc value: [0.50632911 0.37974684 0.40506329 0.3974359 0.43589744 0.37662338 0.37662338 0.37662338 0.41025641 0.4025974 ] mean value: 0.40671965165636054 MCC on Blind test: 0.21 MCC on Training: 0.37 Running classifier: 8 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.05154729 0.06298923 0.04658389 0.04375625 0.05287814 0.05645585 0.05319786 0.05916977 0.05494475 0.05320382] mean value: 0.05347268581390381 key: score_time value: [0.02278543 0.02151632 0.02251029 0.02471995 0.03010249 0.02536869 0.01801205 0.02166367 0.02277279 0.02524614] mean value: 0.02346978187561035 key: test_mcc value: [0.31051721 0.07042952 0.42600643 0.2030906 0.54935027 0.2030906 0.09128709 0.24514517 0.2030906 0.34993386] mean value: 0.2651941348253889 key: train_mcc value: [0.97419246 0.96153201 0.97419246 0.98701299 0.97419246 0.97418375 0.96151265 0.96151265 0.97418375 0.98701078] mean value: 0.9729525962478766 key: test_fscore value: [0.5 0.55555556 0.61538462 0.63157895 0.77777778 0.53333333 0.42857143 0.46153846 0.53333333 0.57142857] mean value: 0.5608502024291497 key: train_fscore value: [0.98684211 0.98013245 0.98684211 0.99346405 0.98684211 0.98666667 0.97986577 0.97986577 0.98666667 0.99337748] mean value: 0.9860565178809384 key: test_precision value: [0.75 0.5 0.8 0.54545455 0.7 0.66666667 0.6 0.75 0.66666667 0.8 ] mean value: 0.677878787878788 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.375 0.625 0.5 0.75 0.875 0.44444444 0.33333333 0.33333333 0.44444444 0.44444444] mean value: 0.5125 key: train_recall value: [0.97402597 0.96103896 0.97402597 0.98701299 0.97402597 0.97368421 0.96052632 0.96052632 0.97368421 0.98684211] mean value: 0.9725393028024605 key: test_accuracy value: [0.64705882 0.52941176 0.70588235 0.58823529 0.76470588 0.58823529 0.52941176 0.58823529 0.58823529 0.64705882] mean value: 0.6176470588235294 key: train_accuracy value: [0.9869281 0.98039216 0.9869281 0.99346405 0.9869281 0.9869281 0.98039216 0.98039216 0.9869281 0.99346405] mean value: 0.9862745098039216 key: test_roc_auc value: [0.63194444 0.53472222 0.69444444 0.59722222 0.77083333 0.59722222 0.54166667 0.60416667 0.59722222 0.65972222] mean value: 0.6229166666666667 key: train_roc_auc value: [0.98701299 0.98051948 0.98701299 0.99350649 0.98701299 0.98684211 0.98026316 0.98026316 0.98684211 0.99342105] mean value: 0.9862696514012302 key: test_jcc value: [0.33333333 0.38461538 0.44444444 0.46153846 0.63636364 0.36363636 0.27272727 0.3 0.36363636 0.4 ] mean value: 0.396029526029526 key: train_jcc value: [0.97402597 0.96103896 0.97402597 0.98701299 0.97402597 0.97368421 0.96052632 0.96052632 0.97368421 0.98684211] mean value: 0.9725393028024605 MCC on Blind test: 0.15 MCC on Training: 0.27 Running classifier: 9 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.0126555 0.00993538 0.00960135 0.00854254 0.00838614 0.00944042 0.00903583 0.00931525 0.00974178 0.00967789] mean value: 0.009633207321166992 key: score_time value: [0.05008602 0.0154047 0.01488972 0.01007867 0.00961971 0.01049089 0.01061225 0.01078701 0.01034975 0.01526213] mean value: 0.01575808525085449 key: test_mcc value: [-0.09128709 -0.16735967 0.40849122 0.24514517 0.30988989 0.54935027 0.18055556 0.09128709 0.30988989 0.34426519] mean value: 0.21802275159522638 key: train_mcc value: [0.50635887 0.50350368 0.42485901 0.45532623 0.44137709 0.50376657 0.50568369 0.50454795 0.43807755 0.50333397] mean value: 0.47868346082140645 key: test_fscore value: [0.30769231 0.5 0.66666667 0.66666667 0.66666667 0.75 0.58823529 0.42857143 0.625 0.36363636] mean value: 0.5563135394017747 key: train_fscore value: [0.73972603 0.75 0.71794872 0.70833333 0.70344828 0.74324324 0.73611111 0.73972603 0.7114094 0.74666667] mean value: 0.7296612798932817 key: test_precision value: [0.4 0.41666667 0.71428571 0.53846154 0.6 0.85714286 0.625 0.6 0.71428571 1. ] mean value: 0.646584249084249 key: train_precision value: [0.7826087 0.76 0.70886076 0.76119403 0.75 0.76388889 0.77941176 0.77142857 0.7260274 0.75675676] mean value: 0.7560176864036965 key: test_recall value: [0.25 0.625 0.625 0.875 0.75 0.66666667 0.55555556 0.33333333 0.55555556 0.22222222] mean value: 0.5458333333333333 key: train_recall value: [0.7012987 0.74025974 0.72727273 0.66233766 0.66233766 0.72368421 0.69736842 0.71052632 0.69736842 0.73684211] mean value: 0.7059295967190703 key: test_accuracy value: [0.47058824 0.41176471 0.70588235 0.58823529 0.64705882 0.76470588 0.58823529 0.52941176 0.64705882 0.58823529] mean value: 0.5941176470588235 key: train_accuracy value: [0.75163399 0.75163399 0.7124183 0.7254902 0.71895425 0.75163399 0.75163399 0.75163399 0.71895425 0.75163399] mean value: 0.738562091503268 key: test_roc_auc value: [0.45833333 0.42361111 0.70138889 0.60416667 0.65277778 0.77083333 0.59027778 0.54166667 0.65277778 0.61111111] mean value: 0.6006944444444444 key: train_roc_auc value: [0.75196514 0.75170882 0.71232057 0.72590567 0.71932673 0.75145249 0.75128161 0.75136705 0.71881408 0.75153794] mean value: 0.7385680109364321 key: test_jcc value: [0.18181818 0.33333333 0.5 0.5 0.5 0.6 0.41666667 0.27272727 0.45454545 0.22222222] mean value: 0.39813131313131317 key: train_jcc value: [0.58695652 0.6 0.56 0.5483871 0.54255319 0.59139785 0.58241758 0.58695652 0.55208333 0.59574468] mean value: 0.5746496777806162 MCC on Blind test: 0.18 MCC on Training: 0.22 Running classifier: 10 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.0213933 0.02722812 0.02797318 0.02879739 0.03134799 0.02772498 0.02733994 0.02762985 0.02668524 0.05125141] mean value: 0.029737138748168947 key: score_time value: [0.01380658 0.01410675 0.01397181 0.01392055 0.01384783 0.01385212 0.0139513 0.01390553 0.02084446 0.02574968] mean value: 0.0157956600189209 key: test_mcc value: [ 0.16903085 0.41666667 0.54935027 0.30988989 0.52777778 -0.03268602 0.54935027 -0.04351941 -0.2030906 -0.07042952] mean value: 0.2172340163583363 key: train_mcc value: [0.93472317 0.89574433 0.94804308 0.92156528 0.9366669 0.94802543 0.92186711 0.90856265 0.92156528 0.93472317] mean value: 0.9271486395616229 key: test_fscore value: [0.53333333 0.70588235 0.77777778 0.66666667 0.75 0.30769231 0.75 0.4 0.5 0.52631579] mean value: 0.5917668227884947 key: train_fscore value: [0.96732026 0.94736842 0.97368421 0.96103896 0.96644295 0.97333333 0.96 0.95364238 0.96052632 0.96732026] mean value: 0.9630677101742627 key: test_precision value: [0.57142857 0.66666667 0.7 0.6 0.75 0.5 0.85714286 0.5 0.45454545 0.5 ] mean value: 0.609978354978355 key: train_precision value: [0.97368421 0.96 0.98666667 0.96103896 1. 0.98648649 0.97297297 0.96 0.96052632 0.96103896] mean value: 0.9722414574519839 key: test_recall value: [0.5 0.75 0.875 0.75 0.75 0.22222222 0.66666667 0.33333333 0.55555556 0.55555556] mean value: 0.5958333333333333 key: train_recall value: [0.96103896 0.93506494 0.96103896 0.96103896 0.93506494 0.96052632 0.94736842 0.94736842 0.96052632 0.97368421] mean value: 0.9542720437457278 key: test_accuracy value: [0.58823529 0.70588235 0.76470588 0.64705882 0.76470588 0.47058824 0.76470588 0.47058824 0.41176471 0.47058824] mean value: 0.6058823529411765 key: train_accuracy value: [0.96732026 0.94771242 0.97385621 0.96078431 0.96732026 0.97385621 0.96078431 0.95424837 0.96078431 0.96732026] mean value: 0.9633986928104574 key: test_roc_auc value: [0.58333333 0.70833333 0.77083333 0.65277778 0.76388889 0.48611111 0.77083333 0.47916667 0.40277778 0.46527778] mean value: 0.6083333333333333 key: train_roc_auc value: [0.96736159 0.94779563 0.97394053 0.96078264 0.96753247 0.97376965 0.9606972 0.95420369 0.96078264 0.96736159] mean value: 0.9634227614490772 key: test_jcc value: [0.36363636 0.54545455 0.63636364 0.5 0.6 0.18181818 0.6 0.25 0.33333333 0.35714286] mean value: 0.43677489177489176 key: train_jcc value: [0.93670886 0.9 0.94871795 0.925 0.93506494 0.94805195 0.92307692 0.91139241 0.92405063 0.93670886] mean value: 0.9288772514405425 MCC on Blind test: 0.12 MCC on Training: 0.22 Running classifier: 11 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.03385139 0.03295922 0.03408384 0.03271246 0.02950668 0.03172851 0.0342648 0.02758145 0.03070116 0.02842641] mean value: 0.03158159255981445 key: score_time value: [0.01169896 0.01146388 0.0115149 0.01151204 0.01164532 0.01152658 0.01150131 0.01150036 0.01151228 0.01150966] mean value: 0.011538529396057129 key: test_mcc value: [ 0.40849122 0.60858062 0.34993386 0.07042952 0.54935027 0.54935027 0.54935027 -0.18184824 0.18055556 0.34993386] mean value: 0.3434127186576892 key: train_mcc value: [0.63397129 0.68730792 0.699419 0.66039994 0.64706889 0.68644222 0.62127983 0.71260054 0.72551702 0.71328417] mean value: 0.6787290817909334 key: test_fscore value: [0.66666667 0.8 0.7 0.55555556 0.77777778 0.75 0.75 0.16666667 0.58823529 0.57142857] mean value: 0.6326330532212884 key: train_fscore value: [0.81818182 0.84 0.8496732 0.82894737 0.82580645 0.84 0.80536913 0.85333333 0.86092715 0.85135135] mean value: 0.8373589805349498 key: test_precision value: [0.71428571 0.66666667 0.58333333 0.5 0.7 0.85714286 0.85714286 0.33333333 0.625 0.8 ] mean value: 0.6636904761904762 key: train_precision value: [0.81818182 0.8630137 0.85526316 0.84 0.82051282 0.85135135 0.82191781 0.86486486 0.86666667 0.875 ] mean value: 0.8476772186321574 key: test_recall value: [0.625 1. 0.875 0.625 0.875 0.66666667 0.66666667 0.11111111 0.55555556 0.44444444] mean value: 0.6444444444444445 key: train_recall value: [0.81818182 0.81818182 0.84415584 0.81818182 0.83116883 0.82894737 0.78947368 0.84210526 0.85526316 0.82894737] mean value: 0.8274606971975393 key: test_accuracy value: [0.70588235 0.76470588 0.64705882 0.52941176 0.76470588 0.76470588 0.76470588 0.41176471 0.58823529 0.64705882] mean value: 0.6588235294117648 key: train_accuracy value: [0.81699346 0.84313725 0.8496732 0.83006536 0.82352941 0.84313725 0.81045752 0.85620915 0.8627451 0.85620915] mean value: 0.8392156862745098 key: test_roc_auc value: [0.70138889 0.77777778 0.65972222 0.53472222 0.77083333 0.77083333 0.77083333 0.43055556 0.59027778 0.65972222] mean value: 0.6666666666666666 key: train_roc_auc value: [0.81698565 0.84330144 0.8497095 0.83014354 0.82347915 0.84304511 0.81032126 0.85611757 0.86269651 0.85603213] mean value: 0.8391831852358168 key: test_jcc value: [0.5 0.66666667 0.53846154 0.38461538 0.63636364 0.6 0.6 0.09090909 0.41666667 0.4 ] mean value: 0.4833682983682984 key: train_jcc value: [0.69230769 0.72413793 0.73863636 0.70786517 0.7032967 0.72413793 0.6741573 0.74418605 0.75581395 0.74117647] mean value: 0.7205715563808073 MCC on Blind test: 0.22 MCC on Training: 0.34 Running classifier: 12 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(cv=3, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(cv=3, random_state=42))]) key: fit_time value: [0.62402844 0.52001715 0.43268514 0.43917704 0.54627109 0.44751525 0.4248507 0.48795199 0.44086552 0.61170244] mean value: 0.4975064754486084 key: score_time value: [0.01186347 0.01175284 0.01176214 0.01183891 0.01205778 0.01189375 0.01176047 0.01205897 0.01197624 0.0118432 ] mean value: 0.011880779266357422 key: test_mcc value: [ 0.04351941 0.60858062 0.34993386 -0.05555556 0.44970061 0.65277778 0.54935027 0.12729377 0.29166667 0.34993386] mean value: 0.3367201280792461 key: train_mcc value: [0.72556391 0.80396515 0.81698565 0.73965143 0.73884807 0.77934127 0.54254956 0.76566357 0.76469583 0.7910755 ] mean value: 0.7468339934178305 key: test_fscore value: [0.42857143 0.8 0.7 0.47058824 0.73684211 0.82352941 0.75 0.33333333 0.66666667 0.57142857] mean value: 0.6280959752321982 key: train_fscore value: [0.8627451 0.90322581 0.90909091 0.86666667 0.86842105 0.88435374 0.77124183 0.87837838 0.88157895 0.89333333] mean value: 0.8719035763522074 key: test_precision value: [0.5 0.66666667 0.58333333 0.44444444 0.63636364 0.875 0.85714286 0.66666667 0.66666667 0.8 ] mean value: 0.6696284271284271 key: train_precision value: [0.86842105 0.8974359 0.90909091 0.89041096 0.88 0.91549296 0.76623377 0.90277778 0.88157895 0.90540541] mean value: 0.8816847672594346 key: test_recall value: [0.375 1. 0.875 0.5 0.875 0.77777778 0.66666667 0.22222222 0.66666667 0.44444444] mean value: 0.6402777777777777 key: train_recall value: [0.85714286 0.90909091 0.90909091 0.84415584 0.85714286 0.85526316 0.77631579 0.85526316 0.88157895 0.88157895] mean value: 0.8626623376623377 key: test_accuracy value: [0.52941176 0.76470588 0.64705882 0.47058824 0.70588235 0.82352941 0.76470588 0.52941176 0.64705882 0.64705882] mean value: 0.6529411764705882 key: train_accuracy value: [0.8627451 0.90196078 0.90849673 0.86928105 0.86928105 0.88888889 0.77124183 0.88235294 0.88235294 0.89542484] mean value: 0.8732026143790851 key: test_roc_auc value: [0.52083333 0.77777778 0.65972222 0.47222222 0.71527778 0.82638889 0.77083333 0.54861111 0.64583333 0.65972222] mean value: 0.6597222222222221 key: train_roc_auc value: [0.86278195 0.90191388 0.90849282 0.86944634 0.8693609 0.88867054 0.77127478 0.88217703 0.88234792 0.89533493] mean value: 0.8731801093643201 key: test_jcc value: [0.27272727 0.66666667 0.53846154 0.30769231 0.58333333 0.7 0.6 0.2 0.5 0.4 ] mean value: 0.47688811188811187 key: train_jcc value: [0.75862069 0.82352941 0.83333333 0.76470588 0.76744186 0.79268293 0.62765957 0.78313253 0.78823529 0.80722892] mean value: 0.7746570418769402 MCC on Blind test: 0.23 MCC on Training: 0.34 Running classifier: 13 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [0.66068411 0.86217093 0.74057102 0.73045516 0.88729548 0.68394828 0.67951345 0.85610986 0.70472693 0.70617819] mean value: 0.7511653423309326 key: score_time value: [0.01223993 0.01211524 0.01215577 0.01214504 0.01232076 0.01230979 0.01207805 0.01222658 0.01230073 0.01258588] mean value: 0.012247776985168457 key: test_mcc value: [ 0.16903085 0.34993386 0.34993386 0.18055556 0.18055556 0.44970061 0.30988989 -0.03268602 0.29166667 0.44970061] mean value: 0.2698281434406391 key: train_mcc value: [1. 0.98701299 0.98701299 1. 0.98701299 1. 0.98701078 1. 0.98701078 1. ] mean value: 0.9935060524650472 key: test_fscore value: [0.53333333 0.7 0.7 0.58823529 0.58823529 0.66666667 0.625 0.30769231 0.66666667 0.66666667] mean value: 0.6042496229260935 key: train_fscore value: [1. 0.99346405 0.99346405 1. 0.99346405 1. 0.99337748 1. 0.99337748 1. ] mean value: 0.9967147123750163 key: test_precision value: [0.57142857 0.58333333 0.58333333 0.55555556 0.55555556 0.83333333 0.71428571 0.5 0.66666667 0.83333333] mean value: 0.6396825396825397 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 0.875 0.875 0.625 0.625 0.55555556 0.55555556 0.22222222 0.66666667 0.55555556] mean value: 0.6055555555555556 key: train_recall value: [1. 0.98701299 0.98701299 1. 0.98701299 1. 0.98684211 1. 0.98684211 1. ] mean value: 0.9934723171565276 key: test_accuracy value: [0.58823529 0.64705882 0.64705882 0.58823529 0.58823529 0.70588235 0.64705882 0.47058824 0.64705882 0.70588235] mean value: 0.623529411764706 key: train_accuracy value: [1. 0.99346405 0.99346405 1. 0.99346405 1. 0.99346405 1. 0.99346405 1. ] mean value: 0.9967320261437909 key: test_roc_auc value: [0.58333333 0.65972222 0.65972222 0.59027778 0.59027778 0.71527778 0.65277778 0.48611111 0.64583333 0.71527778] mean value: 0.6298611111111111 key: train_roc_auc value: [1. 0.99350649 0.99350649 1. 0.99350649 1. 0.99342105 1. 0.99342105 1. ] mean value: 0.9967361585782639 key: test_jcc value: [0.36363636 0.53846154 0.53846154 0.41666667 0.41666667 0.5 0.45454545 0.18181818 0.5 0.5 ] mean value: 0.44102564102564107 key: train_jcc value: [1. 0.98701299 0.98701299 1. 0.98701299 1. 0.98684211 1. 0.98684211 1. ] mean value: 0.9934723171565276 MCC on Blind test: 0.14 MCC on Training: 0.27 Running classifier: 14 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.01224184 0.0120163 0.00900483 0.00890231 0.00844836 0.00874805 0.00852227 0.00861478 0.00995421 0.00858855] mean value: 0.009504151344299317 key: score_time value: [0.01151061 0.01072788 0.00873184 0.00836015 0.00847197 0.00826144 0.00863075 0.00844073 0.00828958 0.00867367] mean value: 0.009009861946105957 key: test_mcc value: [ 0.52777778 0.54935027 0.52297636 0.41666667 0.52777778 0.2030906 0.44970061 -0.18055556 0.07042952 0.43643578] mean value: 0.3523649804141921 key: train_mcc value: [0.44006459 0.45176213 0.41190579 0.43796992 0.47735431 0.42481203 0.38615855 0.42583024 0.41237073 0.38615855] mean value: 0.4254386833203502 key: test_fscore value: [0.75 0.77777778 0.76190476 0.70588235 0.75 0.53333333 0.66666667 0.44444444 0.5 0.5 ] mean value: 0.6390009337068161 key: train_fscore value: [0.70748299 0.72 0.7133758 0.71895425 0.73684211 0.71052632 0.68027211 0.69863014 0.69387755 0.68027211] mean value: 0.7060233364488052 key: test_precision value: [0.75 0.7 0.61538462 0.66666667 0.75 0.66666667 0.83333333 0.44444444 0.57142857 1. ] mean value: 0.6997924297924298 key: train_precision value: [0.74285714 0.73972603 0.7 0.72368421 0.74666667 0.71052632 0.70422535 0.72857143 0.71830986 0.70422535] mean value: 0.721879235518857 key: test_recall value: [0.75 0.875 1. 0.75 0.75 0.44444444 0.55555556 0.44444444 0.44444444 0.33333333] mean value: 0.6347222222222222 key: train_recall value: [0.67532468 0.7012987 0.72727273 0.71428571 0.72727273 0.71052632 0.65789474 0.67105263 0.67105263 0.65789474] mean value: 0.6913875598086124 key: test_accuracy value: [0.76470588 0.76470588 0.70588235 0.70588235 0.76470588 0.58823529 0.70588235 0.41176471 0.52941176 0.64705882] mean value: 0.6588235294117648 key: train_accuracy value: [0.71895425 0.7254902 0.70588235 0.71895425 0.73856209 0.7124183 0.69281046 0.7124183 0.70588235 0.69281046] mean value: 0.7124183006535948 key: test_roc_auc value: [0.76388889 0.77083333 0.72222222 0.70833333 0.76388889 0.59722222 0.71527778 0.40972222 0.53472222 0.66666667] mean value: 0.6652777777777779 key: train_roc_auc value: [0.71924129 0.72564935 0.70574163 0.71898496 0.73863636 0.71240602 0.69258373 0.71214969 0.70565619 0.69258373] mean value: 0.7123632946001368 key: test_jcc value: [0.6 0.63636364 0.61538462 0.54545455 0.6 0.36363636 0.5 0.28571429 0.33333333 0.33333333] mean value: 0.48132201132201125 key: train_jcc value: [0.54736842 0.5625 0.55445545 0.56122449 0.58333333 0.55102041 0.51546392 0.53684211 0.53125 0.51546392] mean value: 0.5458922038204407 MCC on Blind test: 0.26 MCC on Training: 0.35 Running classifier: 15 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.01034927 0.00914383 0.00989604 0.00926137 0.00948048 0.00966668 0.009691 0.00904465 0.0096643 0.0096736 ] mean value: 0.00958712100982666 key: score_time value: [0.0096457 0.00986576 0.00908446 0.00929785 0.00867486 0.00926495 0.00893712 0.00904822 0.00886607 0.00905514] mean value: 0.009174013137817382 key: test_mcc value: [-0.09128709 0.04351941 0.30988989 0.29166667 -0.09128709 0.2030906 0.34993386 -0.31051721 -0.16903085 0.09128709] mean value: 0.06272652710228618 key: train_mcc value: [0.56071878 0.49515701 0.48035238 0.44633936 0.46940184 0.42481203 0.44831039 0.47028954 0.48078755 0.45805453] mean value: 0.47342234114878456 key: test_fscore value: [0.30769231 0.42857143 0.66666667 0.625 0.30769231 0.53333333 0.57142857 0.15384615 0.375 0.42857143] mean value: 0.4397802197802198 key: train_fscore value: [0.74074074 0.70149254 0.6504065 0.64 0.71328671 0.71052632 0.68148148 0.70503597 0.71830986 0.69565217] mean value: 0.6956932296967878 key: test_precision value: [0.4 0.5 0.6 0.625 0.4 0.66666667 0.8 0.25 0.42857143 0.6 ] mean value: 0.5270238095238096 key: train_precision value: [0.86206897 0.8245614 0.86956522 0.83333333 0.77272727 0.71052632 0.77966102 0.77777778 0.77272727 0.77419355] mean value: 0.7977142124108697 key: test_recall value: [0.25 0.375 0.75 0.625 0.25 0.44444444 0.44444444 0.11111111 0.33333333 0.33333333] mean value: 0.3916666666666667 key: train_recall value: [0.64935065 0.61038961 0.51948052 0.51948052 0.66233766 0.71052632 0.60526316 0.64473684 0.67105263 0.63157895] mean value: 0.6224196855775803 key: test_accuracy value: [0.47058824 0.52941176 0.64705882 0.64705882 0.47058824 0.58823529 0.64705882 0.35294118 0.41176471 0.52941176] mean value: 0.5294117647058824 key: train_accuracy value: [0.77124183 0.73856209 0.71895425 0.70588235 0.73202614 0.7124183 0.71895425 0.73202614 0.73856209 0.7254902 ] mean value: 0.7294117647058824 key: test_roc_auc value: [0.45833333 0.52083333 0.65277778 0.64583333 0.45833333 0.59722222 0.65972222 0.36805556 0.41666667 0.54166667] mean value: 0.5319444444444444 key: train_roc_auc value: [0.77204375 0.73940533 0.72026658 0.70710868 0.73248462 0.71240602 0.71821599 0.73145933 0.73812372 0.72488038] mean value: 0.7296394395078606 key: test_jcc value: [0.18181818 0.27272727 0.5 0.45454545 0.18181818 0.36363636 0.4 0.08333333 0.23076923 0.27272727] mean value: 0.29413752913752916 key: train_jcc value: [0.58823529 0.54022989 0.48192771 0.47058824 0.55434783 0.55102041 0.51685393 0.54444444 0.56043956 0.53333333] mean value: 0.534142063036444 MCC on Blind test: 0.19 MCC on Training: 0.06 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=12, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.00995708 0.01418543 0.01331806 0.01381016 0.0147264 0.01486254 0.0145123 0.01362872 0.01432061 0.01429367] mean value: 0.013761496543884278 key: score_time value: [0.00828099 0.01143932 0.01148367 0.01150227 0.01157713 0.01160073 0.01172781 0.01153374 0.01158786 0.0116601 ] mean value: 0.011239361763000489 key: test_mcc value: [0.07042952 0.34426519 0.34993386 0.43643578 0.34426519 0.52777778 0.53673944 0.23570226 0.16735967 0.16903085] mean value: 0.31819395292068464 key: train_mcc value: [0.47486509 0.48783873 0.70059181 0.49874691 0.30126523 0.6984034 0.64878141 0.56847377 0.63529919 0.55109181] mean value: 0.5565357357542894 key: test_fscore value: [0.55555556 0.69565217 0.7 0.72727273 0.69565217 0.77777778 0.8 0.2 0.66666667 0.63157895] mean value: 0.6450156022467236 key: train_fscore value: [0.7638191 0.77083333 0.81203008 0.77486911 0.71028037 0.85542169 0.83333333 0.65486726 0.82758621 0.78947368] mean value: 0.7792514155602677 key: test_precision value: [0.5 0.53333333 0.58333333 0.57142857 0.53333333 0.77777778 0.72727273 1. 0.58333333 0.6 ] mean value: 0.640981240981241 key: train_precision value: [0.62295082 0.64347826 0.96428571 0.64912281 0.55474453 0.78888889 0.76086957 1. 0.73469388 0.65789474] mean value: 0.7376929195891806 key: test_recall value: [0.625 1. 0.875 1. 1. 0.77777778 0.88888889 0.11111111 0.77777778 0.66666667] mean value: 0.7722222222222223 key: train_recall value: [0.98701299 0.96103896 0.7012987 0.96103896 0.98701299 0.93421053 0.92105263 0.48684211 0.94736842 0.98684211] mean value: 0.8873718386876283 key: test_accuracy value: [0.52941176 0.58823529 0.64705882 0.64705882 0.58823529 0.76470588 0.76470588 0.52941176 0.58823529 0.58823529] mean value: 0.6235294117647058 key: train_accuracy value: [0.69281046 0.7124183 0.83660131 0.71895425 0.59477124 0.84313725 0.81699346 0.74509804 0.80392157 0.73856209] mean value: 0.7503267973856209 key: test_roc_auc value: [0.53472222 0.61111111 0.65972222 0.66666667 0.61111111 0.76388889 0.75694444 0.55555556 0.57638889 0.58333333] mean value: 0.6319444444444444 key: train_roc_auc value: [0.69087491 0.71078264 0.83749146 0.71736159 0.5921907 0.84372864 0.81766917 0.74342105 0.80485304 0.7401743 ] mean value: 0.7498547505126453 key: test_jcc value: [0.38461538 0.53333333 0.53846154 0.57142857 0.53333333 0.63636364 0.66666667 0.11111111 0.5 0.46153846] mean value: 0.4936852036852037 key: train_jcc value: [0.61788618 0.62711864 0.6835443 0.63247863 0.55072464 0.74736842 0.71428571 0.48684211 0.70588235 0.65217391] mean value: 0.6418304903473004 MCC on Blind test: 0.2 MCC on Training: 0.32 Running classifier: 17 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.01918006 0.01897621 0.01936412 0.01936817 0.0183723 0.01961446 0.01981544 0.01872444 0.01926327 0.0194025 ] mean value: 0.019208097457885744 key: score_time value: [0.01201344 0.01195312 0.01206374 0.01207852 0.01197147 0.01202536 0.01210523 0.0120008 0.0120151 0.01202369] mean value: 0.012025046348571777 key: test_mcc value: [-0.18055556 0.30988989 0.04351941 0.40849122 -0.2030906 0.76388889 -0.30988989 -0.04351941 -0.29166667 0.2030906 ] mean value: 0.07001578897854496 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.375 0.66666667 0.42857143 0.66666667 0.28571429 0.88888889 0.42105263 0.4 0.35294118 0.53333333] mean value: 0.5018835077890805 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.375 0.6 0.5 0.71428571 0.33333333 0.88888889 0.4 0.5 0.375 0.66666667] mean value: 0.5353174603174604 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.375 0.75 0.375 0.625 0.25 0.88888889 0.44444444 0.33333333 0.33333333 0.44444444] mean value: 0.4819444444444444 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.41176471 0.64705882 0.52941176 0.70588235 0.41176471 0.88235294 0.35294118 0.47058824 0.35294118 0.58823529] mean value: 0.5352941176470587 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.40972222 0.65277778 0.52083333 0.70138889 0.40277778 0.88194444 0.34722222 0.47916667 0.35416667 0.59722222] mean value: 0.5347222222222223 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.23076923 0.5 0.27272727 0.5 0.16666667 0.8 0.26666667 0.25 0.21428571 0.36363636] mean value: 0.35647519147519147 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.05 MCC on Training: 0.07 Running classifier: 18 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42))]) key: fit_time value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( [0.58944321 0.58811402 0.59888887 0.68280673 0.57700896 0.61971188 0.58385444 0.58854127 0.59762406 0.61529183] mean value: 0.6041285276412964 key: score_time value: [0.12862897 0.18787789 0.14885116 0.16871071 0.15245819 0.14249587 0.15442395 0.16406536 0.14169693 0.15326285] mean value: 0.15424718856811523 key: test_mcc value: [0.53673944 0.52297636 0.65277778 0.2030906 0.65277778 0.54935027 0.52777778 0.30988989 0.29166667 0.24514517] mean value: 0.44921917260769284 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.71428571 0.76190476 0.82352941 0.63157895 0.82352941 0.75 0.77777778 0.625 0.66666667 0.46153846] mean value: 0.7035811153071216 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.83333333 0.61538462 0.77777778 0.54545455 0.77777778 0.85714286 0.77777778 0.71428571 0.66666667 0.75 ] mean value: 0.7315601065601066 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.625 1. 0.875 0.75 0.875 0.66666667 0.77777778 0.55555556 0.66666667 0.33333333] mean value: 0.7125 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.76470588 0.70588235 0.82352941 0.58823529 0.82352941 0.76470588 0.76470588 0.64705882 0.64705882 0.58823529] mean value: 0.711764705882353 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.75694444 0.72222222 0.82638889 0.59722222 0.82638889 0.77083333 0.76388889 0.65277778 0.64583333 0.60416667] mean value: 0.7166666666666666 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.55555556 0.61538462 0.7 0.46153846 0.7 0.6 0.63636364 0.45454545 0.5 0.3 ] mean value: 0.5523387723387723 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.21 MCC on Training: 0.45 Running classifier: 19 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea...age_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42))]) key: fit_time value: [0.94619322 0.89613318 0.92241502 0.92348123 0.91271567 0.93205333 0.94888282 0.98260236 0.9951086 0.92188597] mean value: 0.9381471395492553 key: score_time value: [0.29234862 0.20727658 0.15145659 0.20716596 0.18550873 0.20311451 0.24137616 0.19612265 0.21227312 0.19639874] mean value: 0.20930416584014894 key: test_mcc value: [0.53673944 0.52297636 0.65277778 0.30988989 0.88888889 0.44970061 0.6479516 0.18055556 0.29166667 0.43643578] mean value: 0.49175825688373037 key: train_mcc value: [0.90856265 0.8562639 0.88241328 0.90857826 0.92156528 0.92186711 0.86959495 0.86927546 0.88243336 0.8562639 ] mean value: 0.8876818144729285 key: test_fscore value: [0.71428571 0.76190476 0.82352941 0.66666667 0.94117647 0.66666667 0.84210526 0.58823529 0.66666667 0.5 ] mean value: 0.7171236915818959 key: train_fscore value: [0.95483871 0.92903226 0.94193548 0.95424837 0.96103896 0.96 0.93506494 0.93421053 0.94117647 0.92715232] mean value: 0.9438698028514692 key: test_precision value: [0.83333333 0.61538462 0.77777778 0.6 0.88888889 0.83333333 0.8 0.625 0.66666667 1. ] mean value: 0.7640384615384617 key: train_precision value: [0.94871795 0.92307692 0.93589744 0.96052632 0.96103896 0.97297297 0.92307692 0.93421053 0.93506494 0.93333333] mean value: 0.9427916275284696 key: test_recall value: [0.625 1. 0.875 0.75 1. 0.55555556 0.88888889 0.55555556 0.66666667 0.33333333] mean value: 0.725 key: train_recall value: [0.96103896 0.93506494 0.94805195 0.94805195 0.96103896 0.94736842 0.94736842 0.93421053 0.94736842 0.92105263] mean value: 0.9450615174299385 key: test_accuracy value: [0.76470588 0.70588235 0.82352941 0.64705882 0.94117647 0.70588235 0.82352941 0.58823529 0.64705882 0.64705882] mean value: 0.7294117647058824 key: train_accuracy value: [0.95424837 0.92810458 0.94117647 0.95424837 0.96078431 0.96078431 0.93464052 0.93464052 0.94117647 0.92810458] mean value: 0.9437908496732026 key: test_roc_auc value: [0.75694444 0.72222222 0.82638889 0.65277778 0.94444444 0.71527778 0.81944444 0.59027778 0.64583333 0.66666667] mean value: 0.7340277777777777 key: train_roc_auc value: [0.95420369 0.92805878 0.94113124 0.95428913 0.96078264 0.9606972 0.93472317 0.93463773 0.94121668 0.92805878] mean value: 0.94377990430622 key: test_jcc value: [0.55555556 0.61538462 0.7 0.5 0.88888889 0.5 0.72727273 0.41666667 0.5 0.33333333] mean value: 0.5737101787101786 key: train_jcc value: [0.91358025 0.86746988 0.8902439 0.9125 0.925 0.92307692 0.87804878 0.87654321 0.88888889 0.86419753] mean value: 0.8939549362065036 MCC on Blind test: 0.22 MCC on Training: 0.49 Running classifier: 20 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.04530716 0.03857279 0.0417223 0.05312872 0.01561809 0.01573801 0.03318882 0.03973603 0.0403223 0.03439713] mean value: 0.03577313423156738 key: score_time value: [0.01312828 0.03984928 0.02012467 0.01384735 0.01368022 0.01359248 0.02807021 0.02475882 0.02570271 0.02099442] mean value: 0.021374845504760744 key: test_mcc value: [-0.09128709 0.60858062 0.34993386 0.05555556 0.52777778 0.54935027 0.41666667 -0.02151657 0.04351941 0.44970061] mean value: 0.28882810993522623 key: train_mcc value: [0.75170882 0.76491718 0.80399863 0.73855092 0.72556391 0.83011452 0.71260054 0.76491718 0.80396515 0.81698565] mean value: 0.7713322496454527 key: test_fscore value: [0.30769231 0.8 0.7 0.5 0.75 0.75 0.70588235 0.18181818 0.6 0.66666667] mean value: 0.5962059509118333 key: train_fscore value: [0.87581699 0.88461538 0.90196078 0.87012987 0.8627451 0.91390728 0.85333333 0.88 0.90066225 0.90789474] mean value: 0.8851065737161529 key: test_precision value: [0.4 0.66666667 0.58333333 0.5 0.75 0.85714286 0.75 0.5 0.54545455 0.83333333] mean value: 0.6385930735930736 key: train_precision value: [0.88157895 0.87341772 0.90789474 0.87012987 0.86842105 0.92 0.86486486 0.89189189 0.90666667 0.90789474] mean value: 0.889276048875649 key: test_recall value: [0.25 1. 0.875 0.5 0.75 0.66666667 0.66666667 0.11111111 0.66666667 0.55555556] mean value: 0.6041666666666667 key: train_recall value: [0.87012987 0.8961039 0.8961039 0.87012987 0.85714286 0.90789474 0.84210526 0.86842105 0.89473684 0.90789474] mean value: 0.8810663021189337 key: test_accuracy value: [0.47058824 0.76470588 0.64705882 0.52941176 0.76470588 0.76470588 0.70588235 0.47058824 0.52941176 0.70588235] mean value: 0.6352941176470589 key: train_accuracy value: [0.87581699 0.88235294 0.90196078 0.86928105 0.8627451 0.91503268 0.85620915 0.88235294 0.90196078 0.90849673] mean value: 0.8856209150326798 key: test_roc_auc value: [0.45833333 0.77777778 0.65972222 0.52777778 0.76388889 0.77083333 0.70833333 0.49305556 0.52083333 0.71527778] mean value: 0.6395833333333333 key: train_roc_auc value: [0.87585441 0.88226247 0.90199932 0.86927546 0.86278195 0.91498633 0.85611757 0.88226247 0.90191388 0.90849282] mean value: 0.8855946684894052 key: test_jcc value: [0.18181818 0.66666667 0.53846154 0.33333333 0.6 0.6 0.54545455 0.1 0.42857143 0.5 ] mean value: 0.44943056943056947 key: train_jcc value: [0.77906977 0.79310345 0.82142857 0.77011494 0.75862069 0.84146341 0.74418605 0.78571429 0.81927711 0.8313253 ] mean value: 0.7944303575828815 MCC on Blind test: 0.21 MCC on Training: 0.29 Running classifier: 21 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=3))]) key: fit_time value: [0.05335689 0.03910851 0.06473708 0.12464333 0.11036944 0.14698863 0.08838367 0.08918047 0.09083867 0.09165025] mean value: 0.08992569446563721 key: score_time value: [0.01183772 0.0117898 0.02357459 0.02274513 0.02289224 0.02302194 0.02257109 0.02156115 0.02281904 0.02347183] mean value: 0.02062845230102539 key: test_mcc value: [-0.09128709 0.69631062 0.34993386 0.05555556 0.52777778 0.34993386 0.54935027 -0.02151657 0.04351941 0.44970061] mean value: 0.2909278293242059 key: train_mcc value: [0.75170882 0.59510808 0.80399863 0.73855092 0.72556391 0.62206818 0.58180893 0.76491718 0.80396515 0.81698565] mean value: 0.7204675451369167 key: test_fscore value: [0.30769231 0.84210526 0.7 0.5 0.75 0.57142857 0.75 0.18181818 0.6 0.66666667] mean value: 0.5869710990763622 key: train_fscore value: [0.87581699 0.80254777 0.90196078 0.87012987 0.8627451 0.80272109 0.78666667 0.88 0.90066225 0.90789474] mean value: 0.8591145260247275 key: test_precision value: [0.4 0.72727273 0.58333333 0.5 0.75 0.8 0.85714286 0.5 0.54545455 0.83333333] mean value: 0.6496536796536797 key: train_precision value: [0.88157895 0.7875 0.90789474 0.87012987 0.86842105 0.83098592 0.7972973 0.89189189 0.90666667 0.90789474] mean value: 0.8650261115162895 key: test_recall value: [0.25 1. 0.875 0.5 0.75 0.44444444 0.66666667 0.11111111 0.66666667 0.55555556] mean value: 0.5819444444444445 key: train_recall value: [0.87012987 0.81818182 0.8961039 0.87012987 0.85714286 0.77631579 0.77631579 0.86842105 0.89473684 0.90789474] mean value: 0.8535372522214626 key: test_accuracy value: [0.47058824 0.82352941 0.64705882 0.52941176 0.76470588 0.64705882 0.76470588 0.47058824 0.52941176 0.70588235] mean value: 0.6352941176470588 key: train_accuracy value: [0.87581699 0.79738562 0.90196078 0.86928105 0.8627451 0.81045752 0.79084967 0.88235294 0.90196078 0.90849673] mean value: 0.8601307189542483 key: test_roc_auc value: [0.45833333 0.83333333 0.65972222 0.52777778 0.76388889 0.65972222 0.77083333 0.49305556 0.52083333 0.71527778] mean value: 0.6402777777777777 key: train_roc_auc value: [0.87585441 0.7972488 0.90199932 0.86927546 0.86278195 0.81023582 0.7907553 0.88226247 0.90191388 0.90849282] mean value: 0.860082023239918 key: test_jcc value: [0.18181818 0.72727273 0.53846154 0.33333333 0.6 0.4 0.6 0.1 0.42857143 0.5 ] mean value: 0.4409457209457209 key: train_jcc value: [0.77906977 0.67021277 0.82142857 0.77011494 0.75862069 0.67045455 0.64835165 0.78571429 0.81927711 0.8313253 ] mean value: 0.7554569626170821 MCC on Blind test: 0.23 MCC on Training: 0.29 Running classifier: 22 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.01949549 0.01058125 0.011415 0.01059842 0.01063919 0.01213503 0.0110724 0.01135731 0.01164174 0.01099491] mean value: 0.011993074417114257 key: score_time value: [0.01051927 0.00955009 0.00969338 0.00932479 0.00998759 0.0101037 0.00977755 0.01003647 0.00980616 0.01007152] mean value: 0.009887051582336426 key: test_mcc value: [ 0.41666667 0.52297636 0.30988989 0.2030906 0.69631062 0.2030906 0.30988989 0.07042952 -0.07042952 0.52297636] mean value: 0.31848909952573135 key: train_mcc value: [0.60879266 0.60879266 0.62098428 0.6223905 0.6485802 0.64706889 0.6602839 0.6898344 0.6747699 0.62127983] mean value: 0.6402777220445124 key: test_fscore value: [0.70588235 0.76190476 0.66666667 0.63157895 0.84210526 0.53333333 0.625 0.5 0.52631579 0.61538462] mean value: 0.6408171730230553 key: train_fscore value: [0.8 0.8 0.81045752 0.80536913 0.81879195 0.82119205 0.82666667 0.83333333 0.84076433 0.80536913] mean value: 0.8161944101872475 key: test_precision value: [0.66666667 0.61538462 0.6 0.54545455 0.72727273 0.66666667 0.71428571 0.57142857 0.5 1. ] mean value: 0.6607159507159507 key: train_precision value: [0.82191781 0.82191781 0.81578947 0.83333333 0.84722222 0.82666667 0.83783784 0.88235294 0.81481481 0.82191781] mean value: 0.8323770714393091 key: test_recall value: [0.75 1. 0.75 0.75 1. 0.44444444 0.55555556 0.44444444 0.55555556 0.44444444] mean value: 0.6694444444444445 key: train_recall value: [0.77922078 0.77922078 0.80519481 0.77922078 0.79220779 0.81578947 0.81578947 0.78947368 0.86842105 0.78947368] mean value: 0.8014012303485988 key: test_accuracy value: [0.70588235 0.70588235 0.64705882 0.58823529 0.82352941 0.58823529 0.64705882 0.52941176 0.47058824 0.70588235] mean value: 0.6411764705882353 key: train_accuracy value: [0.80392157 0.80392157 0.81045752 0.81045752 0.82352941 0.82352941 0.83006536 0.84313725 0.83660131 0.81045752] mean value: 0.8196078431372549 key: test_roc_auc value: [0.70833333 0.72222222 0.65277778 0.59722222 0.83333333 0.59722222 0.65277778 0.53472222 0.46527778 0.72222222] mean value: 0.648611111111111 key: train_roc_auc value: [0.80408407 0.80408407 0.81049214 0.81066302 0.82373548 0.82347915 0.82997266 0.84278879 0.83680793 0.81032126] mean value: 0.8196428571428571 key: test_jcc value: [0.54545455 0.61538462 0.5 0.46153846 0.72727273 0.36363636 0.45454545 0.33333333 0.35714286 0.44444444] mean value: 0.48027528027528027 key: train_jcc value: [0.66666667 0.66666667 0.68131868 0.6741573 0.69318182 0.69662921 0.70454545 0.71428571 0.72527473 0.6741573 ] mean value: 0.6896883547164446 MCC on Blind test: 0.18 MCC on Training: 0.32 Running classifier: 23 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.01070404 0.01249647 0.01363873 0.01331806 0.01361465 0.01349235 0.01298308 0.01345253 0.01300764 0.01459241] mean value: 0.013129997253417968 key: score_time value: [0.00914025 0.01088619 0.01106977 0.01178002 0.01133275 0.01140618 0.01146913 0.01151633 0.01143765 0.0114882 ] mean value: 0.01115264892578125 key: test_mcc value: [ 0.49099025 0.60858062 0.2030906 0.52297636 0.34426519 0.42600643 0.24514517 -0.16903085 0.29166667 0.44970061] mean value: 0.34133910472718954 key: train_mcc value: [0.65391753 0.7303474 0.55109181 0.3269699 0.31427639 0.49959704 0.56786741 0.6984034 0.64712919 0.79114682] mean value: 0.578074690199082 key: test_fscore value: [0.54545455 0.8 0.63157895 0.76190476 0.69565217 0.76190476 0.46153846 0.375 0.66666667 0.66666667] mean value: 0.6366366985417329 key: train_fscore value: [0.76190476 0.87116564 0.65517241 0.71698113 0.71361502 0.76923077 0.70967742 0.85542169 0.82352941 0.8961039 ] mean value: 0.7772802158620491 key: test_precision value: [1. 0.66666667 0.54545455 0.61538462 0.53333333 0.66666667 0.75 0.42857143 0.66666667 0.83333333] mean value: 0.6706077256077256 key: train_precision value: [0.97959184 0.8255814 0.97435897 0.56296296 0.55882353 0.6302521 0.91666667 0.78888889 0.81818182 0.88461538] mean value: 0.7939923558010328 key: test_recall value: [0.375 1. 0.75 1. 1. 0.88888889 0.33333333 0.33333333 0.66666667 0.55555556] mean value: 0.6902777777777778 key: train_recall value: [0.62337662 0.92207792 0.49350649 0.98701299 0.98701299 0.98684211 0.57894737 0.93421053 0.82894737 0.90789474] mean value: 0.8249829118250173 key: test_accuracy value: [0.70588235 0.76470588 0.58823529 0.70588235 0.58823529 0.70588235 0.58823529 0.41176471 0.64705882 0.70588235] mean value: 0.6411764705882353 key: train_accuracy value: [0.80392157 0.8627451 0.73856209 0.60784314 0.60130719 0.70588235 0.76470588 0.84313725 0.82352941 0.89542484] mean value: 0.7647058823529412 key: test_roc_auc value: [0.6875 0.77777778 0.59722222 0.72222222 0.61111111 0.69444444 0.60416667 0.41666667 0.64583333 0.71527778] mean value: 0.6472222222222221 key: train_roc_auc value: [0.80510936 0.86235475 0.7401743 0.6053486 0.59876965 0.70770677 0.76349966 0.84372864 0.82356459 0.89550581] mean value: 0.7645762132604238 key: test_jcc value: [0.375 0.66666667 0.46153846 0.61538462 0.53333333 0.61538462 0.3 0.23076923 0.5 0.5 ] mean value: 0.4798076923076923 key: train_jcc value: [0.61538462 0.77173913 0.48717949 0.55882353 0.55474453 0.625 0.55 0.74736842 0.7 0.81176471] mean value: 0.6422004414893079 MCC on Blind test: 0.25 MCC on Training: 0.34 Running classifier: 24 Model_name: XGBoost Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.32270074 0.05743408 0.06016827 0.05857182 0.07103682 0.06099796 0.07497549 0.06501508 0.05981445 0.05844665] mean value: 0.08891613483428955 key: score_time value: [0.0107553 0.01160502 0.01095963 0.01043606 0.01032925 0.0100224 0.01065278 0.01021552 0.01011992 0.01014686] mean value: 0.010524272918701172 key: test_mcc value: [0.6846532 0.44970061 0.54935027 0.30988989 0.41666667 0.54935027 0.65277778 0.07042952 0.29166667 0.52297636] mean value: 0.4497461225354849 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.76923077 0.73684211 0.77777778 0.66666667 0.70588235 0.75 0.82352941 0.5 0.66666667 0.61538462] mean value: 0.7011980365695536 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:463: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_CV['source_data'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:490: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_BT['source_data'] = 'BT' [1. 0.63636364 0.7 0.6 0.66666667 0.85714286 0.875 0.57142857 0.66666667 1. ] mean value: 0.7573268398268398 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.625 0.875 0.875 0.75 0.75 0.66666667 0.77777778 0.44444444 0.66666667 0.44444444] mean value: 0.6875000000000001 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.82352941 0.70588235 0.76470588 0.64705882 0.70588235 0.76470588 0.82352941 0.52941176 0.64705882 0.70588235] mean value: 0.711764705882353 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.8125 0.71527778 0.77083333 0.65277778 0.70833333 0.77083333 0.82638889 0.53472222 0.64583333 0.72222222] mean value: 0.7159722222222221 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.625 0.58333333 0.63636364 0.5 0.54545455 0.6 0.7 0.33333333 0.5 0.44444444] mean value: 0.5467929292929293 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.24 MCC on Training: 0.45 Extracting tts_split_name: 70_30 Total cols in each df: CV df: 8 metaDF: 17 Adding column: Model_name Total cols in bts df: BT_df: 8 First proceeding to rowbind CV and BT dfs: Final output should have: 25 columns Combinig 2 using pd.concat by row ~ rowbind Checking Dims of df to combine: Dim of CV: (24, 8) Dim of BT: (24, 8) 8 Number of Common columns: 8 These are: ['source_data', 'Precision', 'JCC', 'MCC', 'Recall', 'Accuracy', 'F1', 'ROC_AUC'] Concatenating dfs with different resampling methods [WF]: Split type: 70_30 No. of dfs combining: 2 PASS: 2 dfs successfully combined nrows in combined_df_wf: 48 ncols in combined_df_wf: 8 PASS: proceeding to merge metadata with CV and BT dfs Adding column: Model_name ========================================================= SUCCESS: Ran multiple classifiers ======================================================= ============================================================== Running several classification models (n): 24 List of models: ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('Gaussian NB', GaussianNB()) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('K-Nearest Neighbors', KNeighborsClassifier()) ('LDA', LinearDiscriminantAnalysis()) ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(cv=3, random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Multinomial', MultinomialNB()) ('Naive Bayes', BernoulliNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=12, random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Random Forest', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42)) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=3)) ('SVC', SVC(random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=12, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ================================================================ Running classifier: 1 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.27293444 0.26321936 0.26224995 0.26529288 0.26042175 0.27000999 0.25779724 0.24534845 0.2551024 0.25881815] mean value: 0.2611194610595703 key: score_time value: [0.01743293 0.01716781 0.01619458 0.01727843 0.01614141 0.01764941 0.01567268 0.01584554 0.01651669 0.01783347] mean value: 0.016773295402526856 key: test_mcc value: [0.63318071 0.82060994 0.71488145 0.73484692 0.88420483 0.68262876 0.75573182 0.69751845 0.81159785 0.80450134] mean value: 0.7539702070244516 key: train_mcc value: [0.88747367 0.90567826 0.84328854 0.88255699 0.89085013 0.85388189 0.87363025 0.82965045 0.90577877 0.88488655] mean value: 0.8757675514336082 key: test_fscore value: [0.82 0.91262136 0.86 0.86868687 0.94230769 0.8490566 0.88 0.83870968 0.90566038 0.90566038] mean value: 0.8782702956127781 key: train_fscore value: [0.94456763 0.95343681 0.9222097 0.94209354 0.94621295 0.92833517 0.93777778 0.91544532 0.95353982 0.94314381] mean value: 0.9386762529891242 key: test_precision value: [0.80392157 0.87037037 0.84313725 0.86 0.89090909 0.78947368 0.8627451 0.88636364 0.82758621 0.84210526] mean value: 0.8476612173476699 key: train_precision value: [0.92207792 0.93073593 0.91498881 0.92358079 0.91507431 0.90149893 0.9173913 0.9082774 0.93088553 0.92560175] mean value: 0.9190112681446923 key: test_recall value: [0.83673469 0.95918367 0.87755102 0.87755102 1. 0.91836735 0.89795918 0.79591837 1. 0.97959184] mean value: 0.9142857142857143 key: train_recall value: [0.96818182 0.97727273 0.92954545 0.96136364 0.97954545 0.95681818 0.95909091 0.92272727 0.97732426 0.96136364] mean value: 0.9593233353947639 key: test_accuracy value: [0.81632653 0.90816327 0.85714286 0.86734694 0.93877551 0.83673469 0.87755102 0.84693878 0.89690722 0.89690722] mean value: 0.8742794024826426 key: train_accuracy value: [0.94318182 0.95227273 0.92159091 0.94090909 0.94431818 0.92613636 0.93636364 0.91477273 0.9523269 0.94211124] mean value: 0.9373983593024457 key: test_roc_auc value: [0.81632653 0.90816327 0.85714286 0.86734694 0.93877551 0.83673469 0.87755102 0.84693878 0.89795918 0.89604592] mean value: 0.8742984693877551 key: train_roc_auc value: [0.94318182 0.95227273 0.92159091 0.94090909 0.94431818 0.92613636 0.93636364 0.91477273 0.9522985 0.94213307] mean value: 0.9373977015048442 key: test_jcc value: [0.69491525 0.83928571 0.75438596 0.76785714 0.89090909 0.73770492 0.78571429 0.72222222 0.82758621 0.82758621] mean value: 0.7848167006963915 key: train_jcc [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. 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Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.6s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. 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Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.6s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.6s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 2.3s remaining: 4.5s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 2.3s remaining: 4.6s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 2.3s remaining: 4.7s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 2.4s remaining: 4.7s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 2.4s remaining: 4.8s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 2.4s remaining: 4.9s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 2.5s remaining: 0.8s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 2.5s remaining: 0.8s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 2.5s remaining: 5.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 2.5s remaining: 5.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 2.5s remaining: 5.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 2.5s remaining: 0.8s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 2.5s remaining: 0.8s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 2.5s remaining: 0.8s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 2.5s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 2.5s remaining: 5.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 2.6s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 2.6s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 2.6s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 2.6s remaining: 0.9s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 2.6s remaining: 0.9s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 2.6s remaining: 0.9s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 2.6s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 2.6s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 2.6s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 2.6s remaining: 0.9s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 2.6s remaining: 0.9s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 2.6s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 2.6s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 2.7s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.6s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished value: [0.89495798 0.91101695 0.85564854 0.89052632 0.89791667 0.86625514 0.88284519 0.84407484 0.91120507 0.89240506] mean value: 0.884685176404601 MCC on Blind test: 0.31 MCC on Training: 0.75 Running classifier: 2 Model_name: Bagging Classifier Model func: BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3))]) key: fit_time value: [0.44331217 0.56176114 0.53802991 0.54191446 0.48823094 0.52417183 0.52480888 0.56480455 0.48455024 0.5215776 ] mean value: 0.5193161725997925 key: score_time value: [0.04934645 0.04650331 0.06498504 0.04029799 0.08377957 0.06017613 0.07326031 0.04177547 0.04141665 0.0718534 ] mean value: 0.05733942985534668 key: test_mcc value: [0.93897107 0.95998366 0.9797959 0.92144268 0.88420483 0.90267093 0.94053994 1. 0.93997522 0.93990077] mean value: 0.9407485003618697 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.96907216 0.98 0.98989899 0.96078431 0.94230769 0.95145631 0.97029703 1. 0.96969697 0.97029703] mean value: 0.9703810500663149 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.97916667 0.96078431 0.98 0.9245283 0.89090909 0.90740741 0.94230769 1. 0.94117647 0.94230769] mean value: 0.9468587635799066 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.95918367 1. 1. 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9959183673469388 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.96938776 0.97959184 0.98979592 0.95918367 0.93877551 0.94897959 0.96938776 1. 0.96907216 0.96907216] mean value: 0.9693246370713234 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.96938776 0.97959184 0.98979592 0.95918367 0.93877551 0.94897959 0.96938776 1. 0.96938776 0.96875 ] mean value: 0.9693239795918368 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.94 0.96078431 0.98 0.9245283 0.89090909 0.90740741 0.94230769 1. 0.94117647 0.94230769] mean value: 0.9429420969132399 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.28 MCC on Training: 0.94 Running classifier: 3 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.06087828 0.04357386 0.0436914 0.04182839 0.04226851 0.03905964 0.04394078 0.0433569 0.04329681 0.0394311 ] mean value: 0.044132566452026366 key: score_time value: [0.00911093 0.00886965 0.00903606 0.00903773 0.00934577 0.00888038 0.00903201 0.00892353 0.00904536 0.00922441] mean value: 0.009050583839416504 key: test_mcc value: [0.85875386 0.94053994 0.8660254 0.94053994 0.8304548 0.90267093 0.84811452 0.92144268 0.82911571 0.81085503] mean value: 0.8748512824630212 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.93069307 0.97029703 0.93333333 0.97029703 0.91588785 0.95145631 0.9245283 0.96078431 0.91428571 0.90740741] mean value: 0.937897036049851 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.90384615 0.94230769 0.875 0.94230769 0.84482759 0.90740741 0.85964912 0.9245283 0.84210526 0.83050847] mean value: 0.8872487694503819 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.95918367 1. 1. 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9959183673469388 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.92857143 0.96938776 0.92857143 0.96938776 0.90816327 0.94897959 0.91836735 0.95918367 0.90721649 0.89690722] mean value: 0.9334735956238166 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.92857143 0.96938776 0.92857143 0.96938776 0.90816327 0.94897959 0.91836735 0.95918367 0.90816327 0.89583333] mean value: 0.9334608843537415 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.87037037 0.94230769 0.875 0.94230769 0.84482759 0.90740741 0.85964912 0.9245283 0.84210526 0.83050847] mean value: 0.8839011911028036 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.14 MCC on Training: 0.87 Running classifier: 4 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.01354337 0.01310015 0.01253462 0.01348186 0.01368451 0.01371026 0.01367044 0.01296258 0.01328015 0.01316094] mean value: 0.013312888145446778 key: score_time value: [0.00980783 0.01021385 0.00965571 0.0104413 0.0102427 0.0103364 0.00995111 0.01016307 0.00899816 0.00947714] mean value: 0.009928727149963379 key: test_mcc value: [0.83953666 0.81302949 0.88420483 0.8304548 0.8660254 0.81302949 0.88420483 0.88420483 0.82911571 0.84638896] mean value: 0.8490195002563196 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.92156863 0.90740741 0.94230769 0.91588785 0.93333333 0.90740741 0.94230769 0.94230769 0.91428571 0.9245283 ] mean value: 0.9251341719162003 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.88679245 0.83050847 0.89090909 0.84482759 0.875 0.83050847 0.89090909 0.89090909 0.84210526 0.85964912] mean value: 0.8642118646881812 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.95918367 1. 1. 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9959183673469388 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.91836735 0.89795918 0.93877551 0.90816327 0.92857143 0.89795918 0.93877551 0.93877551 0.90721649 0.91752577] mean value: 0.9192089206816748 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.91836735 0.89795918 0.93877551 0.90816327 0.92857143 0.89795918 0.93877551 0.93877551 0.90816327 0.91666667] mean value: 0.9192176870748299 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.85454545 0.83050847 0.89090909 0.84482759 0.875 0.83050847 0.89090909 0.89090909 0.84210526 0.85964912] mean value: 0.8609871648597078 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.13 MCC on Training: 0.85 Running classifier: 5 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.17662668 0.16918969 0.16604638 0.16739321 0.16686845 0.17168069 0.1717453 0.17422152 0.17424512 0.17337155] mean value: 0.17113885879516602 key: score_time value: [0.01941586 0.02024126 0.02004719 0.01907945 0.02078485 0.01930785 0.0193944 0.02046394 0.01974559 0.02032566] mean value: 0.01988060474395752 key: test_mcc value: [0.93897107 1. 1. 0.9797959 0.9797959 0.9797959 0.95998366 1. 1. 0.97958324] mean value: 0.9817925664116913 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.96907216 1. 1. 0.98989899 0.98989899 0.98989899 0.98 1. 1. 0.98989899] mean value: 0.9908668124544413 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.97916667 1. 1. 0.98 0.98 0.98 0.96078431 1. 1. 0.98 ] mean value: 0.9859950980392156 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.95918367 1. 1. 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9959183673469388 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.96938776 1. 1. 0.98979592 0.98979592 0.98979592 0.97959184 1. 1. 0.98969072] mean value: 0.9908058068588261 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.96938776 1. 1. 0.98979592 0.98979592 0.98979592 0.97959184 1. 1. 0.98958333] mean value: 0.9907950680272111 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.94 1. 1. 0.98 0.98 0.98 0.96078431 1. 1. 0.98 ] mean value: 0.982078431372549 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.09 MCC on Training: 0.98 Running classifier: 6 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [1.03687024 1.0293467 1.03765845 1.02564192 1.02992272 1.03601766 1.03262448 1.03725505 1.03325844 1.03228235] mean value: 1.0330878019332885 key: score_time value: [0.00938487 0.00943232 0.0094924 0.00931644 0.00926733 0.00933838 0.00953794 0.00915742 0.00947356 0.00918722] mean value: 0.009358787536621093 key: test_mcc value: [0.85732141 0.92144268 0.93897107 0.94053994 0.8660254 0.73854895 0.93897107 0.92144268 0.92071912 0.86452058] mean value: 0.8908502893213053 key: train_mcc value: [0.98411378 0.9932049 0.98185876 0.9887002 0.98421547 0.97727273 0.98411378 0.98421547 0.99321253 0.98423351] mean value: 0.9855141133921608 key: test_fscore value: [0.92783505 0.96078431 0.96969697 0.97029703 0.93333333 0.87378641 0.96969697 0.96078431 0.96 0.93333333] mean value: 0.945954772252794 key: train_fscore value: [0.99207248 0.99660249 0.99095023 0.99435028 0.99210823 0.98863636 0.99207248 0.99210823 0.99661017 0.99210823] mean value: 0.9927619183692917 key: test_precision value: [0.9375 0.9245283 0.96 0.94230769 0.875 0.83333333 0.96 0.9245283 0.92307692 0.875 ] mean value: 0.9155274552491534 key: train_precision value: [0.98871332 0.99322799 0.98648649 0.98876404 0.98434004 0.98863636 0.98871332 0.98434004 0.99324324 0.98434004] mean value: 0.9880804900077607 key: test_recall value: [0.91836735 1. 0.97959184 1. 1. 0.91836735 0.97959184 1. 1. 1. ] mean value: 0.9795918367346939 key: train_recall value: [0.99545455 1. 0.99545455 1. 1. 0.98863636 0.99545455 1. 1. 1. ] mean value: 0.9975000000000002 key: test_accuracy value: [0.92857143 0.95918367 0.96938776 0.96938776 0.92857143 0.86734694 0.96938776 0.95918367 0.95876289 0.92783505] mean value: 0.9437618346307595 key: train_accuracy value: [0.99204545 0.99659091 0.99090909 0.99431818 0.99204545 0.98863636 0.99204545 0.99204545 0.99659478 0.99205448] mean value: 0.9927285625838408 key: test_roc_auc value: [0.92857143 0.95918367 0.96938776 0.96938776 0.92857143 0.86734694 0.96938776 0.95918367 0.95918367 0.92708333] mean value: 0.9437287414965988 key: train_roc_auc value: [0.99204545 0.99659091 0.99090909 0.99431818 0.99204545 0.98863636 0.99204545 0.99204545 0.99659091 0.99206349] mean value: 0.9927290764790765 key: test_jcc value: [0.86538462 0.9245283 0.94117647 0.94230769 0.875 0.77586207 0.94117647 0.9245283 0.92307692 0.875 ] mean value: 0.8988040844684804 key: train_jcc value: [0.98426966 0.99322799 0.98206278 0.98876404 0.98434004 0.97752809 0.98426966 0.98434004 0.99324324 0.98434004] mean value: 0.9856385609385303 MCC on Blind test: 0.37 MCC on Training: 0.89 Running classifier: 7 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.01103067 0.01253223 0.01287222 0.01199532 0.01248765 0.01157308 0.01195478 0.01197243 0.01267314 0.0126164 ] mean value: 0.012170791625976562 key: score_time value: [0.00978971 0.00982094 0.00996375 0.00942349 0.00999284 0.01003337 0.00976372 0.00939131 0.00952911 0.00942087] mean value: 0.009712910652160645 key: test_mcc value: [0.32659863 0.43182096 0.36803737 0.3880785 0.55519838 0.22524154 0.53339646 0.4152274 0.38562517 0.3655232 ] mean value: 0.39947476232418666 key: train_mcc value: [0.4635827 0.41294832 0.4299465 0.48697273 0.43034291 0.48721499 0.4763416 0.43800014 0.43869286 0.43134031] mean value: 0.44953830579239407 key: test_fscore value: [0.65979381 0.69565217 0.67368421 0.7 0.76086957 0.62745098 0.75268817 0.6741573 0.65909091 0.65934066] mean value: 0.6862727788327263 key: train_fscore value: [0.71684588 0.68292683 0.70847851 0.73659674 0.66492829 0.735363 0.72727273 0.70546318 0.70616114 0.69417476] mean value: 0.7078211051955708 key: test_precision value: [0.66666667 0.74418605 0.69565217 0.68627451 0.81395349 0.60377358 0.79545455 0.75 0.725 0.71428571] mean value: 0.7195246729913272 key: train_precision value: [0.75566751 0.73684211 0.72446556 0.75598086 0.77981651 0.75845411 0.75675676 0.73880597 0.73945409 0.74479167] mean value: 0.7491035138906323 key: test_recall value: [0.65306122 0.65306122 0.65306122 0.71428571 0.71428571 0.65306122 0.71428571 0.6122449 0.60416667 0.6122449 ] mean value: 0.6583758503401361 key: train_recall value: [0.68181818 0.63636364 0.69318182 0.71818182 0.57954545 0.71363636 0.7 0.675 0.67573696 0.65 ] mean value: 0.672346423417852 key: test_accuracy value: [0.66326531 0.71428571 0.68367347 0.69387755 0.7755102 0.6122449 0.76530612 0.70408163 0.69072165 0.68041237] mean value: 0.6983378918577741 key: train_accuracy value: [0.73068182 0.70454545 0.71477273 0.74318182 0.70795455 0.74318182 0.7375 0.71818182 0.7185017 0.71396141] mean value: 0.7232463110102157 key: test_roc_auc value: [0.66326531 0.71428571 0.68367347 0.69387755 0.7755102 0.6122449 0.76530612 0.70408163 0.68983844 0.68112245] mean value: 0.6983205782312923 key: train_roc_auc value: [0.73068182 0.70454545 0.71477273 0.74318182 0.70795455 0.74318182 0.7375 0.71818182 0.7185503 0.71388889] mean value: 0.723243918779633 key: test_jcc value: [0.49230769 0.53333333 0.50793651 0.53846154 0.61403509 0.45714286 0.60344828 0.50847458 0.49152542 0.49180328] mean value: 0.5238468571451821 key: train_jcc value: [0.55865922 0.51851852 0.54856115 0.58302583 0.49804688 0.58148148 0.57142857 0.54495413 0.54578755 0.53159851] mean value: 0.5482061832882171 MCC on Blind test: 0.23 MCC on Training: 0.4 Running classifier: 8 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.57983327 0.49019718 0.40206146 0.39907622 0.36751676 0.50740314 0.35108805 0.36062026 0.38835835 0.49600315] mean value: 0.434215784072876 key: score_time value: [0.04838538 0.04385257 0.02082109 0.0321486 0.04093361 0.03688955 0.02089739 0.02151632 0.04245877 0.03688741] mean value: 0.034479069709777835 key: test_mcc value: [0.81649658 0.80195322 0.84307902 0.79582243 0.86164044 0.74740932 0.80195322 0.83953666 0.79430697 0.81085503] mean value: 0.8113052875640234 key: train_mcc value: [0.96633095 0.93918228 0.94827847 0.9503535 0.95268864 0.94784723 0.94802358 0.94792559 0.94379143 0.95303885] mean value: 0.9497460528671551 key: test_fscore value: [0.90909091 0.90384615 0.92307692 0.89908257 0.93203883 0.87850467 0.90384615 0.92156863 0.89719626 0.90740741] mean value: 0.9075658513056762 key: train_fscore value: [0.98320269 0.9698324 0.97430168 0.97533632 0.97648376 0.9740699 0.97418631 0.97412823 0.97212932 0.97658863] mean value: 0.9750259239992947 key: test_precision value: [0.9 0.85454545 0.87272727 0.81666667 0.88888889 0.81034483 0.85454545 0.88679245 0.81355932 0.83050847] mean value: 0.8528578814400303 key: train_precision value: [0.96909492 0.95384615 0.95824176 0.96238938 0.96247241 0.96644295 0.96230599 0.96436526 0.95614035 0.95842451] mean value: 0.961372367591413 key: test_recall value: [0.91836735 0.95918367 0.97959184 1. 0.97959184 0.95918367 0.95918367 0.95918367 1. 1. ] mean value: 0.9714285714285713 key: train_recall value: [0.99772727 0.98636364 0.99090909 0.98863636 0.99090909 0.98181818 0.98636364 0.98409091 0.98866213 0.99545455] mean value: 0.9890934858792002 key: test_accuracy value: [0.90816327 0.89795918 0.91836735 0.8877551 0.92857143 0.86734694 0.89795918 0.91836735 0.88659794 0.89690722] mean value: 0.9007994950557544 key: train_accuracy value: [0.98295455 0.96931818 0.97386364 0.975 0.97613636 0.97386364 0.97386364 0.97386364 0.97162316 0.97616345] mean value: 0.9746650242493036 key: test_roc_auc value: [0.90816327 0.89795918 0.91836735 0.8877551 0.92857143 0.86734694 0.89795918 0.91836735 0.8877551 0.89583333] mean value: 0.9008078231292519 key: train_roc_auc value: [0.98295455 0.96931818 0.97386364 0.975 0.97613636 0.97386364 0.97386364 0.97386364 0.97160379 0.97618532] mean value: 0.9746652752009896 key: test_jcc value: [0.83333333 0.8245614 0.85714286 0.81666667 0.87272727 0.78333333 0.8245614 0.85454545 0.81355932 0.83050847] mean value: 0.831093952137663 key: train_jcc value: [0.96696035 0.94143167 0.94989107 0.95185996 0.95404814 0.94945055 0.94967177 0.9495614 0.94577007 0.95424837] mean value: 0.9512893343000316 MCC on Blind test: 0.18 MCC on Training: 0.81 Running classifier: 9 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.0150435 0.01218581 0.01252317 0.0109148 0.01151371 0.01198387 0.01152873 0.01153588 0.01174688 0.01168919] mean value: 0.01206655502319336 key: score_time value: [0.04005837 0.0182519 0.02155209 0.01689625 0.01626778 0.01666164 0.01982212 0.0204227 0.01725721 0.01753926] mean value: 0.02047293186187744 key: test_mcc value: [0.61858957 0.66017245 0.56286657 0.60358748 0.63632612 0.64311969 0.6228411 0.58639547 0.71070531 0.5981195 ] mean value: 0.6242723269858446 key: train_mcc value: [0.77948925 0.7751001 0.76772426 0.77864998 0.78056775 0.79406152 0.77354264 0.76385729 0.77483312 0.77511359] mean value: 0.7762939492421299 key: test_fscore value: [0.81904762 0.83928571 0.7962963 0.81355932 0.82758621 0.83185841 0.82242991 0.80733945 0.85714286 0.81355932] mean value: 0.8228105100899821 key: train_fscore value: [0.89183673 0.89025641 0.88706366 0.89161554 0.89252815 0.89896907 0.88980433 0.8852459 0.88956434 0.88979592] mean value: 0.8906680043275529 key: test_precision value: [0.76785714 0.74603175 0.72881356 0.69565217 0.71641791 0.734375 0.75862069 0.73333333 0.75 0.69565217] mean value: 0.7326753729473277 key: train_precision value: [0.80925926 0.81121495 0.80898876 0.81040892 0.81191806 0.82264151 0.81355932 0.80597015 0.8040293 0.80740741] mean value: 0.8105397653981331 key: test_recall value: [0.87755102 0.95918367 0.87755102 0.97959184 0.97959184 0.95918367 0.89795918 0.89795918 1. 0.97959184] mean value: 0.9408163265306122 key: train_recall value: [0.99318182 0.98636364 0.98181818 0.99090909 0.99090909 0.99090909 0.98181818 0.98181818 0.99546485 0.99090909] mean value: 0.9884101216244072 key: test_accuracy value: [0.80612245 0.81632653 0.7755102 0.7755102 0.79591837 0.80612245 0.80612245 0.78571429 0.83505155 0.77319588] mean value: 0.7975594361455922 key: train_accuracy value: [0.87954545 0.87840909 0.875 0.87954545 0.88068182 0.88863636 0.87840909 0.87272727 0.87627696 0.87741203] mean value: 0.8786643535238883 key: test_roc_auc value: [0.80612245 0.81632653 0.7755102 0.7755102 0.79591837 0.80612245 0.80612245 0.78571429 0.83673469 0.77104592] mean value: 0.7975127551020408 key: train_roc_auc value: [0.87954545 0.87840909 0.875 0.87954545 0.88068182 0.88863636 0.87840909 0.87272727 0.87614152 0.87754071] mean value: 0.8786636775922491 key: test_jcc value: [0.69354839 0.72307692 0.66153846 0.68571429 0.70588235 0.71212121 0.6984127 0.67692308 0.75 0.68571429] mean value: 0.6992931683538894 key: train_jcc value: [0.80478821 0.80221811 0.79704797 0.80442804 0.80591497 0.8164794 0.80148423 0.79411765 0.80109489 0.80147059] mean value: 0.8029044071874084 MCC on Blind test: 0.13 MCC on Training: 0.62 Running classifier: 10 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.05641031 0.07228279 0.05226612 0.08015513 0.08998704 0.05479908 0.06906104 0.07916236 0.05686259 0.0673573 ] mean value: 0.06783437728881836 key: score_time value: [0.0203228 0.01247954 0.01333809 0.01264334 0.01258731 0.01256061 0.019243 0.0129993 0.0128665 0.01958871] mean value: 0.014862918853759765 key: test_mcc value: [0.47294677 0.73607474 0.53611096 0.48413007 0.64625421 0.63477162 0.63265306 0.46938776 0.58826037 0.68413171] mean value: 0.588472126629768 key: train_mcc value: [0.72567689 0.6757701 0.73060884 0.73037779 0.71577654 0.73699167 0.71793112 0.70641614 0.68011181 0.75797989] mean value: 0.7177640789481355 key: test_fscore value: [0.75 0.86315789 0.78095238 0.76363636 0.83333333 0.82352941 0.81632653 0.73469388 0.79591837 0.85185185] mean value: 0.8013400011785681 key: train_fscore value: [0.86540601 0.84128746 0.86984816 0.86792453 0.86214442 0.8726877 0.86308872 0.85745614 0.84210526 0.8826087 ] mean value: 0.8624557091366517 key: test_precision value: [0.70909091 0.89130435 0.73214286 0.68852459 0.76271186 0.79245283 0.81632653 0.73469388 0.78 0.77966102] mean value: 0.7686908823931664 key: train_precision value: [0.84749455 0.82212581 0.83195021 0.84815618 0.83122363 0.83716075 0.83298097 0.82838983 0.83185841 0.84583333] mean value: 0.8357173680202447 key: test_recall value: [0.79591837 0.83673469 0.83673469 0.85714286 0.91836735 0.85714286 0.81632653 0.73469388 0.8125 0.93877551] mean value: 0.8404336734693878 key: train_recall value: [0.88409091 0.86136364 0.91136364 0.88863636 0.89545455 0.91136364 0.89545455 0.88863636 0.85260771 0.92272727] mean value: 0.8911698618841475 key: test_accuracy value: [0.73469388 0.86734694 0.76530612 0.73469388 0.81632653 0.81632653 0.81632653 0.73469388 0.79381443 0.83505155] mean value: 0.7914580265095729 key: train_accuracy value: [0.8625 0.8375 0.86363636 0.86477273 0.85681818 0.86704545 0.85795455 0.85227273 0.8399546 0.87741203] mean value: 0.8579866628830874 key: test_roc_auc value: [0.73469388 0.86734694 0.76530612 0.73469388 0.81632653 0.81632653 0.81632653 0.73469388 0.7940051 0.83397109] mean value: 0.7913690476190476 key: train_roc_auc value: [0.8625 0.8375 0.86363636 0.86477273 0.85681818 0.86704545 0.85795455 0.85227273 0.83994022 0.87746341] mean value: 0.8579903628117915 key: test_jcc value: [0.6 0.75925926 0.640625 0.61764706 0.71428571 0.7 0.68965517 0.58064516 0.66101695 0.74193548] mean value: 0.6705069799096128 key: train_jcc value: [0.7627451 0.72605364 0.7696737 0.76666667 0.75769231 0.77413127 0.75915222 0.75047985 0.72727273 0.78988327] mean value: 0.7583750748794763 MCC on Blind test: 0.23 MCC on Training: 0.59 Running classifier: 11 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.04473662 0.04490614 0.04461575 0.04532218 0.04290271 0.0436089 0.04506588 0.04342341 0.04318166 0.04522347] mean value: 0.04429867267608643 key: score_time value: [0.01088929 0.01231408 0.01221418 0.01223803 0.01232529 0.01213813 0.01236176 0.01223636 0.01213765 0.01227856] mean value: 0.012113332748413086 key: test_mcc value: [0.36803737 0.55519838 0.57154761 0.5119126 0.49487166 0.46977924 0.51062961 0.41030497 0.50574228 0.60825186] mean value: 0.5006275574182681 key: train_mcc value: [0.6253635 0.59772882 0.60919161 0.62049461 0.63187693 0.59318335 0.58871087 0.65043018 0.60738165 0.60504384] mean value: 0.6129405358795736 key: test_fscore value: [0.69306931 0.76086957 0.78350515 0.76470588 0.76190476 0.74 0.75 0.68817204 0.75510204 0.80808081] mean value: 0.7505409562952849 key: train_fscore value: [0.81564246 0.79863481 0.80630631 0.81129944 0.81715576 0.7963595 0.79266896 0.828125 0.80583614 0.80091533] mean value: 0.8072943695955314 key: test_precision value: [0.67307692 0.81395349 0.79166667 0.73584906 0.71428571 0.7254902 0.76595745 0.72727273 0.74 0.8 ] mean value: 0.748755221916484 key: train_precision value: [0.8021978 0.79954442 0.79910714 0.80674157 0.81165919 0.79726651 0.79907621 0.81359649 0.79777778 0.80645161] mean value: 0.8033418739234746 key: test_recall value: [0.71428571 0.71428571 0.7755102 0.79591837 0.81632653 0.75510204 0.73469388 0.65306122 0.77083333 0.81632653] mean value: 0.7546343537414966 key: train_recall value: [0.82954545 0.79772727 0.81363636 0.81590909 0.82272727 0.79545455 0.78636364 0.84318182 0.81405896 0.79545455] mean value: 0.8114058956916098 key: test_accuracy value: [0.68367347 0.7755102 0.78571429 0.75510204 0.74489796 0.73469388 0.75510204 0.70408163 0.75257732 0.80412371] mean value: 0.7495476541131916 key: train_accuracy value: [0.8125 0.79886364 0.80454545 0.81022727 0.81590909 0.79659091 0.79431818 0.825 0.80363224 0.80249716] mean value: 0.8064083943865441 key: test_roc_auc value: [0.68367347 0.7755102 0.78571429 0.75510204 0.74489796 0.73469388 0.75510204 0.70408163 0.75276361 0.8039966 ] mean value: 0.7495535714285715 key: train_roc_auc value: [0.8125 0.79886364 0.80454545 0.81022727 0.81590909 0.79659091 0.79431818 0.825 0.80362039 0.80248918] mean value: 0.8064064110492681 key: test_jcc value: [0.53030303 0.61403509 0.6440678 0.61904762 0.61538462 0.58730159 0.6 0.52459016 0.60655738 0.6779661 ] mean value: 0.6019253379044842 key: train_jcc value: [0.68867925 0.66477273 0.6754717 0.68250951 0.69083969 0.66162571 0.65654649 0.70666667 0.67481203 0.66793893] mean value: 0.6769862697516683 MCC on Blind test: 0.33 MCC on Training: 0.5 Running classifier: 12 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(cv=3, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(cv=3, random_state=42))]) key: fit_time value: [0.60858488 0.82850003 0.60428119 0.60072947 0.81055665 0.62563396 0.60100174 0.66673374 0.6998775 0.61136556] mean value: 0.6657264709472657 key: score_time value: [0.01252937 0.01246667 0.01239252 0.0123632 0.01248407 0.01258421 0.01254392 0.0138545 0.01226497 0.01256466] mean value: 0.012604808807373047 key: test_mcc value: [0.61339562 0.75510204 0.70588658 0.55851444 0.67016625 0.59266726 0.69402209 0.6951817 0.60962161 0.75096922] mean value: 0.6645526814371469 key: train_mcc value: [0.78279729 0.76518728 0.78630551 0.78389404 0.79328101 0.79600119 0.73765261 0.77668881 0.78067121 0.81360948] mean value: 0.7816088420006465 key: test_fscore value: [0.81188119 0.87755102 0.85981308 0.79245283 0.8440367 0.81081081 0.84848485 0.85148515 0.80808081 0.88073394] mean value: 0.8385330380920957 key: train_fscore value: [0.89356984 0.88546256 0.89594743 0.89473684 0.89934354 0.90076336 0.87196468 0.89108911 0.89258029 0.90889133] mean value: 0.8934348976627474 key: test_precision value: [0.78846154 0.87755102 0.79310345 0.73684211 0.76666667 0.72580645 0.84 0.82692308 0.78431373 0.8 ] mean value: 0.7939668033101565 key: train_precision value: [0.87229437 0.85897436 0.86469345 0.86440678 0.86708861 0.86582809 0.84763948 0.86353945 0.87229437 0.87898089] mean value: 0.8655739851478323 key: test_recall value: [0.83673469 0.87755102 0.93877551 0.85714286 0.93877551 0.91836735 0.85714286 0.87755102 0.83333333 0.97959184] mean value: 0.8914965986394557 key: train_recall value: [0.91590909 0.91363636 0.92954545 0.92727273 0.93409091 0.93863636 0.89772727 0.92045455 0.9138322 0.94090909] mean value: 0.9232014017728304 key: test_accuracy value: [0.80612245 0.87755102 0.84693878 0.7755102 0.82653061 0.78571429 0.84693878 0.84693878 0.80412371 0.86597938] mean value: 0.8282347990742689 key: train_accuracy value: [0.89090909 0.88181818 0.89204545 0.89090909 0.89545455 0.89659091 0.86818182 0.8875 0.88989784 0.90578888] mean value: 0.8899095810545867 key: test_roc_auc value: [0.80612245 0.87755102 0.84693878 0.7755102 0.82653061 0.78571429 0.84693878 0.84693878 0.80442177 0.86479592] mean value: 0.8281462585034014 key: train_roc_auc value: [0.89090909 0.88181818 0.89204545 0.89090909 0.89545455 0.89659091 0.86818182 0.8875 0.88987065 0.9058287 ] mean value: 0.8899108431251289 key: test_jcc value: [0.68333333 0.78181818 0.75409836 0.65625 0.73015873 0.68181818 0.73684211 0.74137931 0.6779661 0.78688525] mean value: 0.7230549550988704 key: train_jcc value: [0.80761523 0.7944664 0.81150794 0.80952381 0.81709742 0.81944444 0.77299413 0.80357143 0.806 0.83299799] mean value: 0.8075218785263634 MCC on Blind test: 0.16 MCC on Training: 0.66 Running classifier: 13 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [3.78632593 3.83117938 3.92059731 3.79532671 3.90354323 3.32177973 3.66438174 3.0530436 3.75086188 4.08359838] mean value: 3.71106379032135 key: score_time value: [0.01305175 0.01252174 0.01268578 0.01295376 0.01267815 0.01256227 0.0127306 0.02322984 0.0178299 0.0127604 ] mean value: 0.01430041790008545 key: test_mcc value: [0.81649658 0.95998366 0.94053994 0.87755102 0.81302949 0.77881794 0.92144268 0.92144268 0.81159785 0.93990077] mean value: 0.8780802596757141 key: train_mcc value: [0.98415445 0.9887002 0.9932049 0.99090909 0.98198051 0.97530483 0.9887002 0.96867228 0.97754985 0.99773243] mean value: 0.9846908749111849 key: test_fscore value: [0.90909091 0.98 0.97029703 0.93877551 0.90740741 0.89090909 0.96078431 0.96078431 0.90566038 0.97029703] mean value: 0.9394005981826901 key: train_fscore value: [0.9920904 0.99435028 0.99660249 0.99545455 0.99099099 0.98765432 0.99435028 0.98434004 0.98878924 0.99886493] mean value: 0.9923487518022492 key: test_precision value: [0.9 0.96078431 0.94230769 0.93877551 0.83050847 0.80327869 0.9245283 0.9245283 0.82758621 0.94230769] mean value: 0.8994605182315955 key: train_precision value: [0.98651685 0.98876404 0.99322799 0.99545455 0.98214286 0.97560976 0.98876404 0.969163 0.97782705 0.99773243] mean value: 0.9855202566382195 key: test_recall value: [0.91836735 1. 1. 0.93877551 1. 1. 1. 1. 1. 1. ] mean value: 0.9857142857142858 key: train_recall value: [0.99772727 1. 1. 0.99545455 1. 1. 1. 1. 1. 1. ] mean value: 0.9993181818181818 key: test_accuracy value: [0.90816327 0.97959184 0.96938776 0.93877551 0.89795918 0.87755102 0.95918367 0.95918367 0.89690722 0.96907216] mean value: 0.9355775299810647 key: train_accuracy value: [0.99204545 0.99431818 0.99659091 0.99545455 0.99090909 0.9875 0.99431818 0.98409091 0.98864926 0.99886493] mean value: 0.992274146114952 key: test_roc_auc value: [0.90816327 0.97959184 0.96938776 0.93877551 0.89795918 0.87755102 0.95918367 0.95918367 0.89795918 0.96875 ] mean value: 0.9356505102040817 key: train_roc_auc value: [0.99204545 0.99431818 0.99659091 0.99545455 0.99090909 0.9875 0.99431818 0.98409091 0.98863636 0.99886621] mean value: 0.9922729849515564 key: test_jcc value: [0.83333333 0.96078431 0.94230769 0.88461538 0.83050847 0.80327869 0.9245283 0.9245283 0.82758621 0.94230769] mean value: 0.8873778390060589 key: train_jcc value: [0.98430493 0.98876404 0.99322799 0.99095023 0.98214286 0.97560976 0.98876404 0.969163 0.97782705 0.99773243] mean value: 0.9848486325974835 MCC on Blind test: 0.2 MCC on Training: 0.88 Running classifier: 14 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.0159514 0.0162282 0.01603913 0.01616836 0.01620817 0.01604342 0.01612806 0.02574563 0.01686382 0.01686168] mean value: 0.01722378730773926 key: score_time value: [0.01330614 0.01307893 0.01230168 0.01322103 0.01304317 0.01228952 0.01310635 0.01331663 0.01244307 0.01242781] mean value: 0.012853431701660156 key: test_mcc value: [0.24535825 0.49236596 0.53072278 0.36742346 0.49236596 0.20619652 0.36742346 0.52117275 0.50532183 0.34013605] mean value: 0.40684870279480717 key: train_mcc value: [0.46820117 0.4456203 0.42729038 0.43414585 0.40150013 0.45247026 0.40106207 0.43866469 0.45292996 0.45307276] mean value: 0.43749575760486464 key: test_fscore value: [0.63366337 0.7311828 0.76767677 0.68686869 0.75728155 0.62857143 0.68041237 0.72727273 0.74468085 0.67346939] mean value: 0.7031079935776179 key: train_fscore value: [0.73287671 0.71889401 0.71232877 0.71477663 0.68646081 0.72202999 0.68867925 0.71771429 0.72519954 0.72202999] mean value: 0.7140989980400911 key: test_precision value: [0.61538462 0.77272727 0.76 0.68 0.72222222 0.58928571 0.6875 0.82051282 0.76086957 0.67346939] mean value: 0.7081971598105138 key: train_precision value: [0.73623853 0.72897196 0.71559633 0.72055427 0.71890547 0.73302108 0.71568627 0.72183908 0.7293578 0.73302108] mean value: 0.7253191877857736 key: test_recall value: [0.65306122 0.69387755 0.7755102 0.69387755 0.79591837 0.67346939 0.67346939 0.65306122 0.72916667 0.67346939] mean value: 0.7014880952380952 key: train_recall value: [0.72954545 0.70909091 0.70909091 0.70909091 0.65681818 0.71136364 0.66363636 0.71363636 0.72108844 0.71136364] mean value: 0.7034724799010512 key: test_accuracy value: [0.62244898 0.74489796 0.76530612 0.68367347 0.74489796 0.60204082 0.68367347 0.75510204 0.75257732 0.67010309] mean value: 0.7024721228697665 key: train_accuracy value: [0.73409091 0.72272727 0.71363636 0.71704545 0.7 0.72613636 0.7 0.71931818 0.72644722 0.72644722] mean value: 0.7185848983593025 key: test_roc_auc value: [0.62244898 0.74489796 0.76530612 0.68367347 0.74489796 0.60204082 0.68367347 0.75510204 0.75233844 0.67006803] mean value: 0.7024447278911564 key: train_roc_auc value: [0.73409091 0.72272727 0.71363636 0.71704545 0.7 0.72613636 0.7 0.71931818 0.72645331 0.72643012] mean value: 0.7185837971552258 key: test_jcc value: [0.46376812 0.57627119 0.62295082 0.52307692 0.609375 0.45833333 0.515625 0.57142857 0.59322034 0.50769231] mean value: 0.5441741596569025 key: train_jcc value: [0.57837838 0.56115108 0.55319149 0.55614973 0.52260398 0.56498195 0.52517986 0.5597148 0.56887299 0.56498195] mean value: 0.5555206195315899 MCC on Blind test: 0.29 MCC on Training: 0.41 Running classifier: 15 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.01611495 0.0162518 0.01605034 0.01616788 0.01605082 0.01617551 0.0161674 0.01617384 0.01671219 0.01705503] mean value: 0.01629197597503662 key: score_time value: [0.01254225 0.01260495 0.01259184 0.01272082 0.01250076 0.01249099 0.01258707 0.01246428 0.01255107 0.01243544] mean value: 0.01254894733428955 key: test_mcc value: [0.24913644 0.20619652 0.34810057 0.44907312 0.28721348 0.34810057 0.51407258 0.25131234 0.34073752 0.26059587] mean value: 0.32545390055654494 key: train_mcc value: [0.39294773 0.39997992 0.39225942 0.37338309 0.35633159 0.36735498 0.35376228 0.40087141 0.37961087 0.39427569] mean value: 0.381077698516571 key: test_fscore value: [0.58426966 0.57142857 0.65957447 0.72164948 0.62365591 0.65957447 0.73913043 0.57471264 0.65217391 0.60869565] mean value: 0.639486521271287 key: train_fscore value: [0.66993865 0.67321867 0.6641791 0.66424242 0.66028708 0.66825208 0.6495098 0.67878788 0.67458432 0.6763285 ] mean value: 0.6679328522606581 key: test_precision value: [0.65 0.61904762 0.68888889 0.72916667 0.65909091 0.68888889 0.79069767 0.65789474 0.68181818 0.65116279] mean value: 0.6816656356359538 key: train_precision value: [0.728 0.73262032 0.73351648 0.71168831 0.6969697 0.70074813 0.70478723 0.72727273 0.70822943 0.72164948] mean value: 0.7165481814991196 key: test_recall value: [0.53061224 0.53061224 0.63265306 0.71428571 0.59183673 0.63265306 0.69387755 0.51020408 0.625 0.57142857] mean value: 0.6033163265306123 key: train_recall value: [0.62045455 0.62272727 0.60681818 0.62272727 0.62727273 0.63863636 0.60227273 0.63636364 0.64399093 0.63636364] mean value: 0.625762729334158 key: test_accuracy value: [0.62244898 0.60204082 0.67346939 0.7244898 0.64285714 0.67346939 0.75510204 0.62244898 0.67010309 0.62886598] mean value: 0.6615295602777194 key: train_accuracy value: [0.69431818 0.69772727 0.69318182 0.68522727 0.67727273 0.68295455 0.675 0.69886364 0.68898978 0.69580023] mean value: 0.6889335465896191 key: test_roc_auc value: [0.62244898 0.60204082 0.67346939 0.7244898 0.64285714 0.67346939 0.75510204 0.62244898 0.66964286 0.62946429] mean value: 0.6615433673469387 key: train_roc_auc value: [0.69431818 0.69772727 0.69318182 0.68522727 0.67727273 0.68295455 0.675 0.69886364 0.68904092 0.69573284] mean value: 0.6889319212533498 key: test_jcc value: [0.41269841 0.4 0.49206349 0.56451613 0.453125 0.49206349 0.5862069 0.40322581 0.48387097 0.4375 ] mean value: 0.47252701966029276 key: train_jcc value: [0.50369004 0.50740741 0.4972067 0.49727768 0.49285714 0.50178571 0.48094374 0.51376147 0.50896057 0.51094891] mean value: 0.5014839367445333 MCC on Blind test: 0.15 MCC on Training: 0.33 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.02339458 0.02512097 0.03339505 0.02721596 0.02362609 0.02771425 0.03517389 0.02338171 0.03210688 0.0317812 ] mean value: 0.02829105854034424 key: score_time value: [0.01226687 0.01221752 0.01223779 0.01222944 0.01215553 0.01220918 0.01228476 0.01228404 0.01234102 0.01224875] mean value: 0.012247490882873534 key: test_mcc value: [0.50262469 0.36843313 0.56286657 0.47871355 0.35165724 0.4472136 0.55377492 0.31622777 0.46618855 0.25943726] mean value: 0.4307137277614583 key: train_mcc value: [0.59541013 0.28382762 0.58888987 0.64085386 0.5052977 0.61812214 0.52605581 0.51535643 0.58319836 0.31567179] mean value: 0.5172683719715625 key: test_fscore value: [0.7706422 0.72727273 0.75 0.76521739 0.69811321 0.75 0.79032258 0.54545455 0.75862069 0.7 ] mean value: 0.7255643343713987 key: train_fscore value: [0.80728051 0.70331447 0.76178344 0.8313253 0.75884956 0.82124617 0.78127813 0.67787115 0.80688337 0.70910556] mean value: 0.7658937653695086 key: test_precision value: [0.7 0.57831325 0.84615385 0.66666667 0.64912281 0.66666667 0.65333333 0.75 0.64705882 0.53846154] mean value: 0.6695776934841056 key: train_precision value: [0.76315789 0.54579674 0.86666667 0.74460432 0.73922414 0.7458256 0.64679583 0.88321168 0.69752066 0.54931336] mean value: 0.7182116882031352 key: test_recall value: [0.85714286 0.97959184 0.67346939 0.89795918 0.75510204 0.85714286 1. 0.42857143 0.91666667 1. ] mean value: 0.83656462585034 key: train_recall value: [0.85681818 0.98863636 0.67954545 0.94090909 0.77954545 0.91363636 0.98636364 0.55 0.9569161 1. ] mean value: 0.8652370645227787 key: test_accuracy value: [0.74489796 0.63265306 0.7755102 0.7244898 0.67346939 0.71428571 0.73469388 0.64285714 0.71134021 0.56701031] mean value: 0.692120765832106 key: train_accuracy value: [0.79545455 0.58295455 0.7875 0.80909091 0.75227273 0.80113636 0.72386364 0.73863636 0.7707151 0.59023837] mean value: 0.7351862552884119 key: test_roc_auc value: [0.74489796 0.63265306 0.7755102 0.7244898 0.67346939 0.71428571 0.73469388 0.64285714 0.71343537 0.5625 ] mean value: 0.6918792517006803 key: train_roc_auc value: [0.79545455 0.58295455 0.7875 0.80909091 0.75227273 0.80113636 0.72386364 0.73863636 0.7705035 0.59070295] mean value: 0.7352115543186971 key: test_jcc value: [0.62686567 0.57142857 0.6 0.61971831 0.53623188 0.6 0.65333333 0.375 0.61111111 0.53846154] mean value: 0.5732150419893471 key: train_jcc value: [0.67684022 0.54239401 0.61522634 0.71134021 0.6114082 0.69670711 0.64106352 0.51271186 0.67628205 0.54931336] mean value: 0.6233286868899862 MCC on Blind test: 0.21 MCC on Training: 0.43 Running classifier: 17 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.03809571 0.04000163 0.05982518 0.06304383 0.04563904 0.04104805 0.03972769 0.04078984 0.04149842 0.04007483] mean value: 0.04497442245483398 key: score_time value: [0.01323485 0.0130043 0.013129 0.02386522 0.01338959 0.01319647 0.01316047 0.01313186 0.01324415 0.01319599] mean value: 0.014255189895629882 key: test_mcc value: [0.93897107 1. 1. 1. 1. 0.9797959 0.9797959 1. 1. 1. ] mean value: 0.9898562862293427 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.96907216 1. 1. 1. 1. 0.98989899 0.98989899 1. 1. 1. ] mean value: 0.9948870144746433 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.97916667 1. 1. 1. 1. 0.98 0.98 1. 1. 1. ] mean value: 0.9939166666666667 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.95918367 1. 1. 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9959183673469388 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.96938776 1. 1. 1. 1. 0.98979592 0.98979592 1. 1. 1. ] mean value: 0.9948979591836735 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.96938776 1. 1. 1. 1. 0.98979592 0.98979592 1. 1. 1. ] mean value: 0.9948979591836735 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.94 1. 1. 1. 1. 0.98 0.98 1. 1. 1. ] mean value: 0.99 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.02 MCC on Training: 0.99 Running classifier: 18 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42))]) key: fit_time value: [0.78436875 0.75258493 0.78289032 0.81158066 0.82807231 0.79341245 0.79715872 0.78194737 0.81080389 0.81108522] mean value: 0.7953904628753662 key: score_time value: [0.19242716 0.20549464 0.2099154 0.27286506 0.22260022 0.18032217 0.18225265 0.1912179 0.18254828 0.17789054] mean value: 0.20175340175628662 key: test_mcc value: [0.93897107 0.9797959 0.9797959 0.95998366 0.95998366 0.90267093 0.95998366 1. 0.97959184 0.93990077] mean value: 0.9600677385028293 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.96907216 0.98989899 0.98989899 0.98 0.98 0.95145631 0.98 1. 0.98969072 0.97029703] mean value: 0.9800314206778499 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.97916667 0.98 0.98 0.96078431 0.96078431 0.90740741 0.96078431 1. 0.97959184 0.94230769] mean value: 0.965082654429293 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.95918367 1. 1. 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9959183673469388 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.96938776 0.98979592 0.98979592 0.97959184 0.97959184 0.94897959 0.97959184 1. 0.98969072 0.96907216] mean value: 0.979549758047549 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.96938776 0.98979592 0.98979592 0.97959184 0.97959184 0.94897959 0.97959184 1. 0.98979592 0.96875 ] mean value: 0.9795280612244899 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.94 0.98 0.98 0.96078431 0.96078431 0.90740741 0.96078431 1. 0.97959184 0.94230769] mean value: 0.9611659877626263 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.27 MCC on Training: 0.96 Running classifier: 19 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea...age_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42))]) key: fit_time value: [1.12264085 1.23749542 1.18532205 1.20947051 1.14078093 1.15355015 1.18694782 1.16918659 1.15509129 1.13463211] mean value: 1.1695117712020875 key: score_time value: [0.24816823 0.2554338 0.24567294 0.16694975 0.24594927 0.26806617 0.27315068 0.27701974 0.19700933 0.28642797] mean value: 0.24638478755950927 key: test_mcc value: [0.87828292 0.95918367 0.91836735 0.94053994 0.86164044 0.88420483 0.81649658 0.9797959 0.93997522 0.9016018 ] mean value: 0.9080088653619536 key: train_mcc /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( value: [0.9864044 0.98637383 0.98411378 0.99095004 0.98411378 0.9932049 0.98409345 0.98645536 0.99096016 0.98871309] mean value: 0.9875382792373758 key: test_fscore value: [0.9375 0.97959184 0.95918367 0.97029703 0.93203883 0.94230769 0.90909091 0.98989899 0.96969697 0.95145631] mean value: 0.9541062246532681 key: train_fscore value: [0.99321267 0.99319728 0.99207248 0.99547511 0.99207248 0.99660249 0.99203641 0.99322799 0.99548533 0.99435028] mean value: 0.9937732519361612 key: test_precision value: [0.95744681 0.97959184 0.95918367 0.94230769 0.88888889 0.89090909 0.9 0.98 0.94117647 0.90740741] mean value: 0.9346911868816032 key: train_precision value: [0.98873874 0.99095023 0.98871332 0.99099099 0.98871332 0.99322799 0.99316629 0.98654709 0.99101124 0.98876404] mean value: 0.9900823236630194 key: test_recall value: [0.91836735 0.97959184 0.95918367 1. 0.97959184 1. 0.91836735 1. 1. 1. ] mean value: 0.9755102040816327 key: train_recall value: [0.99772727 0.99545455 0.99545455 1. 0.99545455 1. 0.99090909 1. 1. 1. ] mean value: 0.9974999999999999 key: test_accuracy value: [0.93877551 0.97959184 0.95918367 0.96938776 0.92857143 0.93877551 0.90816327 0.98979592 0.96907216 0.94845361] mean value: 0.952977067115506 key: train_accuracy value: [0.99318182 0.99318182 0.99204545 0.99545455 0.99204545 0.99659091 0.99204545 0.99318182 0.9954597 0.99432463] mean value: 0.9937511608709111 key: test_roc_auc value: [0.93877551 0.97959184 0.95918367 0.96938776 0.92857143 0.93877551 0.90816327 0.98979592 0.96938776 0.94791667] mean value: 0.9529549319727891 key: train_roc_auc value: [0.99318182 0.99318182 0.99204545 0.99545455 0.99204545 0.99659091 0.99204545 0.99318182 0.99545455 0.99433107] mean value: 0.9937512883941455 key: test_jcc value: [0.88235294 0.96 0.92156863 0.94230769 0.87272727 0.89090909 0.83333333 0.98 0.94117647 0.90740741] mean value: 0.9131782835900484 key: train_jcc value: [0.98651685 0.98648649 0.98426966 0.99099099 0.98426966 0.99322799 0.98419865 0.98654709 0.99101124 0.98876404] mean value: 0.9876282659922279 MCC on Blind test: 0.42 MCC on Training: 0.91 Running classifier: 20 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.05812669 0.03159118 0.0348196 0.03584218 0.04244256 0.01761842 0.01735067 0.01752758 0.01871681 0.04319048] mean value: 0.031722617149353025 key: score_time value: [0.0262177 0.02938557 0.02950549 0.01923037 0.01224995 0.01225495 0.01265502 0.01221776 0.01898789 0.01955128] mean value: 0.019225597381591797 key: test_mcc value: [0.47095959 0.67572463 0.55147997 0.49827288 0.59988588 0.55286561 0.57154761 0.47095959 0.5704578 0.5341285 ] mean value: 0.5496282057583806 key: train_mcc value: [0.67963147 0.64346273 0.66280482 0.67084448 0.67074737 0.63428916 0.68661904 0.67066416 0.65728409 0.66004446] mean value: 0.6636391771087707 key: test_fscore value: [0.74509804 0.82978723 0.78 0.76635514 0.81132075 0.78431373 0.78787879 0.72340426 0.79207921 0.78504673] mean value: 0.7805283873743024 key: train_fscore value: [0.8410372 0.82418813 0.83608361 0.83798883 0.83762598 0.81930415 0.84529148 0.8372615 0.83014623 0.83296214] mean value: 0.8341889255169497 key: test_precision value: [0.71698113 0.86666667 0.76470588 0.70689655 0.75438596 0.75471698 0.78 0.75555556 0.75471698 0.72413793] mean value: 0.7578763646585688 key: train_precision value: [0.8344519 0.81236203 0.81023454 0.82417582 0.82560706 0.80931264 0.8340708 0.827051 0.82366071 0.81659389] mean value: 0.8217520395814792 key: test_recall value: [0.7755102 0.79591837 0.79591837 0.83673469 0.87755102 0.81632653 0.79591837 0.69387755 0.83333333 0.85714286] mean value: 0.8078231292517006 key: train_recall value: [0.84772727 0.83636364 0.86363636 0.85227273 0.85 0.82954545 0.85681818 0.84772727 0.83673469 0.85 ] mean value: 0.847082560296846 key: test_accuracy value: [0.73469388 0.83673469 0.7755102 0.74489796 0.79591837 0.7755102 0.78571429 0.73469388 0.78350515 0.7628866 ] mean value: 0.7730065221965076 key: train_accuracy value: [0.83977273 0.82159091 0.83068182 0.83522727 0.83522727 0.81704545 0.84318182 0.83522727 0.82860386 0.82973893] mean value: 0.8316297337736044 key: test_roc_auc value: [0.73469388 0.83673469 0.7755102 0.74489796 0.79591837 0.7755102 0.78571429 0.73469388 0.78401361 0.76190476] mean value: 0.7729591836734693 key: train_roc_auc value: [0.83977273 0.82159091 0.83068182 0.83522727 0.83522727 0.81704545 0.84318182 0.83522727 0.82859462 0.8297619 ] mean value: 0.8316311069882498 key: test_jcc value: [0.59375 0.70909091 0.63934426 0.62121212 0.68253968 0.64516129 0.65 0.56666667 0.6557377 0.64615385] mean value: 0.6409656483198921 key: train_jcc value: [0.72568093 0.70095238 0.71833648 0.72115385 0.72061657 0.69391635 0.73203883 0.72007722 0.70961538 0.71374046] mean value: 0.7156128462687081 MCC on Blind test: 0.29 MCC on Training: 0.55 Running classifier: 21 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=3))]) key: fit_time value: [0.13800764 0.11421108 0.13336086 0.16934967 0.14309597 0.13640976 0.14040089 0.13239574 0.13180494 0.13162518] mean value: 0.13706617355346679 key: score_time value: [0.02579498 0.02341032 0.02135444 0.02040291 0.01914501 0.02288437 0.01924992 0.01941228 0.02473688 0.01911831] mean value: 0.021550941467285156 key: test_mcc value: [0.53160953 0.67403108 0.53979562 0.53979562 0.66436384 0.61545745 0.57154761 0.51020408 0.56832741 0.58738439] mean value: 0.5802516629950839 key: train_mcc value: [0.69787328 0.68283473 0.72441305 0.72920376 0.71337283 0.71217523 0.68661904 0.71908123 0.69373735 0.73124562] mean value: 0.7090556095786239 key: test_fscore value: [0.77227723 0.83333333 0.78504673 0.78504673 0.8411215 0.81553398 0.78787879 0.75510204 0.78787879 0.81081081] mean value: 0.7974029922294371 key: train_fscore value: [0.8503937 0.84513274 0.86593407 0.86842105 0.86089814 0.8590455 0.84529148 0.86252772 0.84882419 0.86879824] mean value: 0.8575266825796349 key: test_precision value: [0.75 0.85106383 0.72413793 0.72413793 0.77586207 0.77777778 0.78 0.75510204 0.76470588 0.72580645] mean value: 0.7628593913381666 key: train_precision value: [0.84187082 0.82327586 0.83829787 0.83898305 0.83086681 0.83947939 0.8340708 0.84199134 0.83849558 0.84368308] mean value: 0.8371014606730558 key: test_recall value: [0.79591837 0.81632653 0.85714286 0.85714286 0.91836735 0.85714286 0.79591837 0.75510204 0.8125 0.91836735] mean value: 0.8383928571428572 key: train_recall value: [0.85909091 0.86818182 0.89545455 0.9 0.89318182 0.87954545 0.85681818 0.88409091 0.85941043 0.89545455] mean value: 0.8791228612657184 key: test_accuracy value: [0.76530612 0.83673469 0.76530612 0.76530612 0.82653061 0.80612245 0.78571429 0.75510204 0.78350515 0.78350515] mean value: 0.7873132758257941 key: train_accuracy value: [0.84886364 0.84090909 0.86136364 0.86363636 0.85568182 0.85568182 0.84318182 0.85909091 0.84676504 0.86492622] mean value: 0.8540100350840987 key: test_roc_auc value: [0.76530612 0.83673469 0.76530612 0.76530612 0.82653061 0.80612245 0.78571429 0.75510204 0.78380102 0.78210034] mean value: 0.7872023809523809 key: train_roc_auc value: [0.84886364 0.84090909 0.86136364 0.86363636 0.85568182 0.85568182 0.84318182 0.85909091 0.84675067 0.86496083] mean value: 0.8540120593692022 key: test_jcc value: [0.62903226 0.71428571 0.64615385 0.64615385 0.72580645 0.68852459 0.65 0.60655738 0.65 0.68181818] mean value: 0.6638332265302123 key: train_jcc value: [0.73972603 0.73180077 0.76356589 0.76744186 0.75576923 0.75291829 0.73203883 0.7582846 0.73735409 0.76803119] mean value: 0.7506930774353997 MCC on Blind test: 0.23 MCC on Training: 0.58 Running classifier: 22 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.07164145 0.05526114 0.05824828 0.04669976 0.04805112 0.05670357 0.05779457 0.05629444 0.05391073 0.05322194] mean value: 0.05578269958496094 key: score_time value: [0.02303672 0.0233357 0.01964736 0.01957512 0.0212369 0.02408457 0.02516818 0.02267051 0.02251244 0.02274346] mean value: 0.022401094436645508 key: test_mcc value: [0.55286561 0.72964185 0.59183673 0.59233034 0.53339646 0.51062961 0.57442696 0.50262469 0.60962161 0.64951091] mean value: 0.5846884764774837 key: train_mcc value: [0.71706748 0.71167433 0.71291377 0.70559588 0.70762459 0.70997894 0.7023548 0.69924769 0.73444313 0.71301165] mean value: 0.7113912273867911 key: test_fscore value: [0.76595745 0.84090909 0.79591837 0.8 0.77669903 0.76 0.77419355 0.71264368 0.80808081 0.82828283] mean value: 0.7862684797102407 key: train_fscore value: [0.85380117 0.85351788 0.85076381 0.84813084 0.84982538 0.85081585 0.8436019 0.84371328 0.86659065 0.85111372] mean value: 0.8511874465368681 key: test_precision value: [0.8 0.94871795 0.79591837 0.78431373 0.74074074 0.74509804 0.81818182 0.81578947 0.78431373 0.82 ] mean value: 0.8053073838867736 key: train_precision value: [0.87951807 0.86651054 0.88077859 0.87259615 0.87112172 0.87320574 0.88118812 0.87347932 0.87155963 0.87893462] mean value: 0.87488925088602 key: test_recall value: [0.73469388 0.75510204 0.79591837 0.81632653 0.81632653 0.7755102 0.73469388 0.63265306 0.83333333 0.83673469] mean value: 0.7731292517006803 key: train_recall value: [0.82954545 0.84090909 0.82272727 0.825 0.82954545 0.82954545 0.80909091 0.81590909 0.861678 0.825 ] mean value: 0.8288950731807875 key: test_accuracy value: [0.7755102 0.85714286 0.79591837 0.79591837 0.76530612 0.75510204 0.78571429 0.74489796 0.80412371 0.82474227] mean value: 0.7904376183463075 key: train_accuracy value: [0.85795455 0.85568182 0.85568182 0.85227273 0.85340909 0.85454545 0.85 0.84886364 0.86719637 0.85584563] mean value: 0.8551451088638944 key: test_roc_auc value: [0.7755102 0.85714286 0.79591837 0.79591837 0.76530612 0.75510204 0.78571429 0.74489796 0.80442177 0.82461735] mean value: 0.790454931972789 key: train_roc_auc value: [0.85795455 0.85568182 0.85568182 0.85227273 0.85340909 0.85454545 0.85 0.84886364 0.86720264 0.85581066] mean value: 0.8551422387136671 key: test_jcc value: [0.62068966 0.7254902 0.66101695 0.66666667 0.63492063 0.61290323 0.63157895 0.55357143 0.6779661 0.70689655] mean value: 0.6491700357156043 key: train_jcc value: [0.74489796 0.7444668 0.7402863 0.73630832 0.7388664 0.74036511 0.7295082 0.7296748 0.76458753 0.74081633] mean value: 0.7409777728460826 MCC on Blind test: 0.32 MCC on Training: 0.58 Running classifier: 23 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.0237112 0.04465675 0.04029393 0.03909445 0.05399942 0.03257942 0.03790832 0.03624606 0.03294563 0.03785801] mean value: 0.037929320335388185 key: score_time value: [0.0117979 0.01140308 0.01190805 0.01266074 0.02610636 0.01192856 0.01191092 0.01449656 0.01242661 0.0138638 ] mean value: 0.013850259780883788 key: test_mcc value: [0.47577156 0.56575238 0.74230749 0.5694948 0.49041445 0.43395285 0.59632419 0.61993042 0.42085959 0.63695087] mean value: 0.5551758593478683 key: train_mcc value: [0.65284773 0.5498278 0.65632845 0.62309356 0.52785926 0.64897355 0.60669013 0.63525378 0.54801267 0.65577497] mean value: 0.610466188117306 key: test_fscore value: [0.75471698 0.71604938 0.87619048 0.79674797 0.765625 0.75 0.7826087 0.82051282 0.64197531 0.83018868] mean value: 0.773461531157053 key: train_fscore value: [0.8342246 0.68758916 0.83701367 0.82119205 0.78198198 0.83269231 0.78839178 0.82621083 0.68383405 0.83148559] mean value: 0.7924616009695324 key: test_precision value: [0.70175439 0.90625 0.82142857 0.66216216 0.62025316 0.63380282 0.8372093 0.70588235 0.78787879 0.77192982] mean value: 0.7448551368720965 key: train_precision value: [0.78787879 0.92337165 0.77886497 0.70340357 0.64776119 0.72166667 0.84237726 0.7096248 0.92635659 0.81168831] mean value: 0.785299379027321 key: test_recall value: [0.81632653 0.59183673 0.93877551 1. 1. 0.91836735 0.73469388 0.97959184 0.54166667 0.89795918] mean value: /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:463: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_CV['source_data'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:490: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_BT['source_data'] = 'BT' 0.841921768707483 key: train_recall value: [0.88636364 0.54772727 0.90454545 0.98636364 0.98636364 0.98409091 0.74090909 0.98863636 0.54195011 0.85227273] mean value: 0.8419222840651412 key: test_accuracy value: [0.73469388 0.76530612 0.86734694 0.74489796 0.69387755 0.69387755 0.79591837 0.78571429 0.70103093 0.81443299] mean value: 0.7597096570586997 key: train_accuracy value: [0.82386364 0.75113636 0.82386364 0.78522727 0.725 0.80227273 0.80113636 0.79204545 0.74914869 0.82746879] mean value: 0.7881162934681664 key: test_roc_auc value: [0.73469388 0.76530612 0.86734694 0.74489796 0.69387755 0.69387755 0.79591837 0.78571429 0.69940476 0.81356293] mean value: 0.7594600340136054 key: train_roc_auc value: [0.82386364 0.75113636 0.82386364 0.78522727 0.725 0.80227273 0.80113636 0.79204545 0.74938415 0.82749691] mean value: 0.7881426509997939 key: test_jcc value: [0.60606061 0.55769231 0.77966102 0.66216216 0.62025316 0.6 0.64285714 0.69565217 0.47272727 0.70967742] mean value: 0.6346743266273488 key: train_jcc value: [0.71559633 0.52391304 0.71971067 0.69662921 0.64201183 0.71334432 0.6506986 0.7038835 0.51956522 0.71157495] mean value: 0.6596927674836657 MCC on Blind test: 0.16 MCC on Training: 0.56 Running classifier: 24 Model_name: XGBoost Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.19809055 0.1678915 0.16289949 0.16260386 0.17104459 0.16220713 0.16728377 0.17237735 0.20705199 0.26078486] mean value: 0.18322350978851318 key: score_time value: [0.01125956 0.01145315 0.01158118 0.01237202 0.011199 0.01138997 0.01384354 0.01175213 0.01289201 0.01206803] mean value: 0.011981058120727538 key: test_mcc value: [0.91836735 0.94053994 0.95998366 0.9797959 0.94053994 0.88420483 0.95998366 0.9797959 0.95959175 0.9016018 ] mean value: 0.9424404725741244 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.95918367 0.97029703 0.98 0.98989899 0.97029703 0.94230769 0.98 0.98989899 0.97959184 0.95145631] mean value: 0.9712931552395305 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.95918367 0.94230769 0.96078431 0.98 0.94230769 0.89090909 0.96078431 0.98 0.96 0.90740741] mean value: 0.9483684183852251 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.95918367 1. 1. 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9959183673469388 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.95918367 0.96938776 0.97959184 0.98979592 0.96938776 0.93877551 0.97959184 0.98979592 0.97938144 0.94845361] mean value: 0.9703345255628024 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.95918367 0.96938776 0.97959184 0.98979592 0.96938776 0.93877551 0.97959184 0.98979592 0.97959184 0.94791667] mean value: 0.9703018707482991 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.92156863 0.94230769 0.96078431 0.98 0.94230769 0.89090909 0.96078431 0.98 0.96 0.90740741] mean value: 0.9446069137833846 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.41 MCC on Training: 0.94 Extracting tts_split_name: 70_30 Total cols in each df: CV df: 8 metaDF: 17 Adding column: Model_name Total cols in bts df: BT_df: 8 First proceeding to rowbind CV and BT dfs: Final output should have: 25 columns Combinig 2 using pd.concat by row ~ rowbind Checking Dims of df to combine: Dim of CV: (24, 8) Dim of BT: (24, 8) 8 Number of Common columns: 8 These are: ['source_data', 'Precision', 'JCC', 'MCC', 'Recall', 'Accuracy', 'F1', 'ROC_AUC'] Concatenating dfs with different resampling methods [WF]: Split type: 70_30 No. of dfs combining: 2 PASS: 2 dfs successfully combined nrows in combined_df_wf: 48 ncols in combined_df_wf: 8 PASS: proceeding to merge metadata with CV and BT dfs Adding column: Model_name ========================================================= SUCCESS: Ran multiple classifiers ======================================================= Input params: Dim of input df: (858, 175) Data type to split: actual Split type: 80_20 target colname: dst_mode oversampling enabled PASS: x_features has no target variable and no dst column Dropped cols: 2 These were: dst_mode and dst No. of cols in input df: 175 No.of cols dropped: 2 No. of columns for x_features: 173 ------------------------------------------------------------- Successfully generated training and test data: Data used: actual Split type: 80_20 Total no. of input features: 173 --------No. of numerical features: 167 --------No. of categorical features: 6 =========================== Resampling: NONE Baseline =========================== Total data size: 315 Train data size: (252, 173) y_train numbers: Counter({0: 202, 1: 50}) Test data size: (63, 173) y_test_numbers: Counter({0: 51, 1: 12}) y_train ratio: 4.04 y_test ratio: 4.25 ------------------------------------------------------------- Simple Random OverSampling Counter({0: 202, 1: 202}) (404, 173) Simple Random UnderSampling Counter({0: 50, 1: 50}) (100, 173) Simple Combined Over and UnderSampling Counter({0: 202, 1: 202}) (404, 173) SMOTE_NC OverSampling Counter({0: 202, 1: 202}) (404, 173) Generated Resampled data as below: ================================= Resampling: Random oversampling ================================ Train data size: (404, 173) y_train numbers: 404 y_train ratio: 1.0 y_test ratio: 4.25 ================================ Resampling: Random underampling ================================ Train data size: (100, 173) y_train numbers: 100 y_train ratio: 1.0 y_test ratio: 4.25 ================================ Resampling:Combined (over+under) ================================ Train data size: (404, 173) y_train numbers: 404 y_train ratio: 1.0 y_test ratio: 4.25 ============================== Resampling: Smote NC [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... 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Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 3 of 8 for this parallel run (total 100)... 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Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... 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Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... 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Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished ============================== Train data size: (404, 173) y_train numbers: 404 y_train ratio: 1.0 y_test ratio: 4.25 ------------------------------------------------------------- ============================================================== Running several classification models (n): 24 List of models: ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('Gaussian NB', GaussianNB()) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('K-Nearest Neighbors', KNeighborsClassifier()) ('LDA', LinearDiscriminantAnalysis()) ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(cv=3, random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Multinomial', MultinomialNB()) ('Naive Bayes', BernoulliNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=12, random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Random Forest', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42)) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=3)) ('SVC', SVC(random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=12, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ================================================================ Running classifier: 1 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.11731863 0.12359977 0.12337995 0.12300539 0.12451553 0.12157512 0.12052464 0.1186614 0.1191318 0.11750937] mean value: 0.1209221601486206 key: score_time value: [0.01509118 0.015903 0.01579237 0.01515651 0.016155 0.01528502 0.01472592 0.01552153 0.01530361 0.01467991] mean value: 0.015361404418945313 key: test_mcc value: [ 0.06241878 -0.17622684 0.32732684 0.25 -0.10206207 0.32732684 0.13363062 0.22116293 0.87287156 0.40824829] mean value: 0.23246969388690775 key: train_mcc value: [1. 1. 1. 0.98634974 1. 1. 0.98612105 0.97219807 0.95816782 0.98612105] mean value: 0.9888957732763155 key: test_fscore value: [0.22222222 0. 0.44444444 0.4 0. 0.44444444 0.33333333 0.28571429 0.88888889 0.33333333] mean value: 0.3352380952380953 key: train_fscore value: [1. 1. 1. 0.98901099 1. 1. 0.98876404 0.97727273 0.96629213 0.98876404] mean value: 0.9910103941002818 key: test_precision value: [0.25 0. 0.5 0.4 0. 0.5 0.28571429 0.5 1. 1. ] mean value: 0.4435714285714285 key: train_precision value: [1. 1. 1. 0.97826087 1. 1. 1. 1. 0.97727273 1. ] mean value: 0.9955533596837943 key: test_recall value: [0.2 0. 0.4 0.4 0. 0.4 0.4 0.2 0.8 0.2] mean value: 0.3 key: train_recall value: [1. 1. 1. 1. 1. 1. 0.97777778 0.95555556 0.95555556 0.97777778] mean value: 0.9866666666666667 key: test_accuracy value: [0.73076923 0.69230769 0.8 0.76 0.76 0.8 0.68 0.8 0.96 0.84 ] mean value: 0.7823076923076923 key: train_accuracy value: [1. 1. 1. 0.99559471 1. 1. 0.99559471 0.99118943 0.98678414 0.99559471] mean value: 0.9964757709251103 key: test_roc_auc value: [0.52857143 0.42857143 0.65 0.625 0.475 0.65 0.575 0.575 0.9 0.6 ] mean value: 0.6007142857142858 key: train_roc_auc value: [1. 1. 1. 0.99725275 1. 1. 0.98888889 0.97777778 0.97503053 0.98888889] mean value: 0.9927838827838829 key: test_jcc value: [0.125 0. 0.28571429 0.25 0. 0.28571429 0.2 0.16666667 0.8 0.2 ] mean value: 0.23130952380952383 key: train_jcc value: [1. 1. 1. 0.97826087 1. 1. 0.97777778 0.95555556 0.93478261 0.97777778] mean value: 0.9824154589371981 MCC on Blind test: 0.09 MCC on Training: 0.23 Running classifier: 2 Model_name: Bagging Classifier Model func: BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3))]) key: fit_time value: [0.17655802 0.19371128 0.20039606 0.20327592 0.15950441 0.17311835 0.17474961 0.18609238 0.226825 0.20498633] mean value: 0.18992173671722412 key: score_time value: [0.05751061 0.07619452 0.04081583 0.07699323 0.05407643 0.05307817 0.05113626 0.04002428 0.08616519 0.05496144] mean value: 0.05909559726715088 key: test_mcc value: [ 0. -0.09759001 0.22116293 -0.10206207 -0.10206207 0.12309149 0.22116293 -0.10206207 0. 0. ] mean value: 0.016164113430127355 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.7s remaining: 1.4s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.7s remaining: 1.4s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.7s remaining: 1.5s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.7s remaining: 1.4s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.7s remaining: 1.5s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.7s remaining: 1.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.8s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.8s remaining: 0.3s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.8s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.8s remaining: 1.5s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.8s remaining: 1.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.8s remaining: 0.3s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.8s remaining: 1.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.8s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.8s remaining: 0.3s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.8s remaining: 1.6s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.8s remaining: 0.3s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.8s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.8s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.8s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.8s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.8s remaining: 0.3s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.8s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.8s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.8s remaining: 0.3s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.8s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.8s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.8s remaining: 0.3s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.8s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.9s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [0. 0. 0.28571429 0. 0. 0.25 0.28571429 0. 0. 0. ] mean value: 0.08214285714285716 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0. 0. 0.5 0. 0. 0.33333333 0.5 0. 0. 0. ] mean value: 0.13333333333333333 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0. 0. 0.2 0. 0. 0.2 0.2 0. 0. 0. ] mean value: 0.06000000000000001 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.80769231 0.76923077 0.8 0.76 0.76 0.76 0.8 0.76 0.8 0.8 ] mean value: 0.7816923076923077 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.5 0.47619048 0.575 0.475 0.475 0.55 0.575 0.475 0.5 0.5 ] mean value: 0.5101190476190476 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0. 0. 0.16666667 0. 0. 0.14285714 0.16666667 0. 0. 0. ] mean value: 0.047619047619047616 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.16 MCC on Training: 0.02 Running classifier: 3 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.01733017 0.01922226 0.01855159 0.01677656 0.01777029 0.01730847 0.01751542 0.01857805 0.01985598 0.01996326] mean value: 0.01828720569610596 key: score_time value: [0.0085752 0.00877023 0.00869703 0.00863004 0.00863051 0.00910258 0.00861883 0.00905776 0.0087049 0.00972748] mean value: 0.008851456642150878 key: test_mcc value: [ 0.33290015 -0.07615914 0.32732684 0.30012252 0.12309149 0.18731716 0.22116293 0.18731716 0.05455447 -0.21821789] mean value: 0.14394157015251635 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.44444444 0.16666667 0.44444444 0.46153846 0.25 0.36363636 0.28571429 0.36363636 0.22222222 0. ] mean value: 0.3002303252303252 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.5 0.14285714 0.5 0.375 0.33333333 0.33333333 0.5 0.33333333 0.25 0. ] mean value: 0.3267857142857143 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.4 0.2 0.4 0.6 0.2 0.4 0.2 0.4 0.2 0. ] mean value: 0.30000000000000004 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.80769231 0.61538462 0.8 0.72 0.76 0.72 0.8 0.72 0.72 0.64 ] mean value: 0.7303076923076922 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.65238095 0.45714286 0.65 0.675 0.55 0.6 0.575 0.6 0.525 0.4 ] mean value: 0.568452380952381 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.28571429 0.09090909 0.28571429 0.3 0.14285714 0.22222222 0.16666667 0.22222222 0.125 0. ] mean value: 0.18413059163059162 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.01 MCC on Training: 0.14 Running classifier: 4 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.0102303 0.01007199 0.01043129 0.00943756 0.01058149 0.01054168 0.01027942 0.00947356 0.01053905 0.01060152] mean value: 0.01021878719329834 key: score_time value: [0.00980973 0.00974488 0.00917935 0.00887227 0.00935817 0.00954747 0.00967479 0.00938177 0.00987053 0.00949264] mean value: 0.009493160247802734 key: test_mcc value: [-0.20806259 -0.07615914 0.12309149 -0.12862394 0.13363062 0. 0.25 0.32732684 0.25 -0.04682929] mean value: 0.062437398178327844 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0. 0.16666667 0.25 0.15384615 0.33333333 0.2 0.4 0.44444444 0.4 0.18181818] mean value: 0.253010878010878 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0. 0.14285714 0.33333333 0.125 0.28571429 0.2 0.4 0.5 0.4 0.16666667] mean value: 0.25535714285714284 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0. 0.2 0.2 0.2 0.4 0.2 0.4 0.4 0.4 0.2] mean value: 0.26 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.65384615 0.61538462 0.76 0.56 0.68 0.68 0.76 0.8 0.76 0.64 ] mean value: 0.6909230769230769 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.4047619 0.45714286 0.55 0.425 0.575 0.5 0.625 0.65 0.625 0.475 ] mean value: 0.5286904761904762 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0. 0.09090909 0.14285714 0.08333333 0.2 0.11111111 0.25 0.28571429 0.25 0.1 ] mean value: 0.1513924963924964 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.01 MCC on Training: 0.06 Running classifier: 5 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.10295081 0.10780668 0.10339379 0.10227871 0.10166383 0.10707664 0.10575628 0.10375476 0.10601521 0.10306406] mean value: 0.10437607765197754 key: score_time value: [0.01782131 0.02015662 0.01727605 0.01763749 0.01938772 0.01897717 0.01946211 0.01916695 0.01892233 0.0171814 ] mean value: 0.01859891414642334 key: test_mcc value: [-0.14085904 -0.17622684 0.22116293 -0.10206207 0. 0.05455447 0.58976782 -0.10206207 0.58976782 0.40824829] mean value: 0.13422913145951393 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0. 0. 0.28571429 0. 0. 0.22222222 0.57142857 0. 0.57142857 0.33333333] mean value: 0.19841269841269846 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0. 0. 0.5 0. 0. 0.25 1. 0. 1. 1. ] mean value: 0.375 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0. 0. 0.2 0. 0. 0.2 0.4 0. 0.4 0.2] mean value: 0.14 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.73076923 0.69230769 0.8 0.76 0.8 0.72 0.88 0.76 0.88 0.84 ] mean value: 0.7863076923076923 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.45238095 0.42857143 0.575 0.475 0.5 0.525 0.7 0.475 0.7 0.6 ] mean value: 0.5430952380952381 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0. 0. 0.16666667 0. 0. 0.125 0.4 0. 0.4 0.2 ] mean value: 0.12916666666666668 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.04 MCC on Training: 0.13 Running classifier: 6 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.38523483 0.37834239 0.38432741 0.39656138 0.3817873 0.38228106 0.37963939 0.38292956 0.38520527 0.38197947] mean value: 0.3838288068771362 key: score_time value: [0.00878239 0.00917673 0.00894547 0.00886512 0.00879312 0.00888157 0.00894594 0.00882316 0.00898123 0.00895381] mean value: 0.008914852142333984 key: test_mcc value: [-0.09759001 -0.14085904 0.12309149 0.12309149 0.22116293 -0.14744196 0.12309149 -0.10206207 0.58976782 0. ] mean value: 0.06922521532694634 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0. 0. 0.25 0.25 0.28571429 0. 0.25 0. 0.57142857 0. ] mean value: 0.16071428571428573 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0. 0. 0.33333333 0.33333333 0.5 0. 0.33333333 0. 1. 0. ] mean value: 0.25 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0. 0. 0.2 0.2 0.2 0. 0.2 0. 0.4 0. ] mean value: 0.12000000000000002 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.76923077 0.73076923 0.76 0.76 0.8 0.72 0.76 0.76 0.88 0.8 ] mean value: 0.774 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.47619048 0.45238095 0.55 0.55 0.575 0.45 0.55 0.475 0.7 0.5 ] mean value: 0.5278571428571428 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0. 0. 0.14285714 0.14285714 0.16666667 0. 0.14285714 0. 0.4 0. ] mean value: 0.09952380952380953 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.01 MCC on Training: 0.07 Running classifier: 7 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.00935626 0.01012254 0.0092423 0.00915718 0.0119698 0.00936675 0.00932336 0.00929666 0.00932455 0.0104177 ] mean value: 0.009757709503173829 key: score_time value: [0.00954413 0.0135622 0.00908017 0.00911808 0.00891757 0.00902915 0.00918341 0.00971055 0.00907302 0.00992393] mean value: 0.009714221954345703 key: test_mcc value: [-0.07615914 -0.18516402 0.32732684 -0.04682929 0.13363062 -0.04682929 0.25 -0.16666667 0.40824829 -0.04029115] mean value: 0.055726618936727944 key: train_mcc value: [0.36643293 0.38405401 0.40804663 0.38557353 0.43315624 0.38958441 0.33263247 0.35582988 0.32840606 0.45346877] mean value: 0.38371849308738143 key: test_fscore value: [0.16666667 0.13333333 0.44444444 0.18181818 0.33333333 0.18181818 0.42857143 0.14285714 0.53333333 0.25 ] mean value: 0.2796176046176046 key: train_fscore value: [0.50847458 0.5210084 0.53658537 0.51923077 0.55445545 0.52336449 0.48387097 0.5 0.47619048 0.57142857] mean value: 0.5194609061603804 key: test_precision value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) [0.14285714 0.1 0.5 0.16666667 0.28571429 0.16666667 0.33333333 0.11111111 0.4 0.18181818] mean value: 0.23881673881673876 key: train_precision value: [0.4109589 0.41891892 0.42307692 0.45762712 0.5 0.4516129 0.37974684 0.39240506 0.41666667 0.47761194] mean value: 0.43286252736746567 key: test_recall value: [0.2 0.2 0.4 0.2 0.4 0.2 0.6 0.2 0.8 0.4] mean value: 0.36 key: train_recall value: [0.66666667 0.68888889 0.73333333 0.6 0.62222222 0.62222222 0.66666667 0.68888889 0.55555556 0.71111111] mean value: 0.6555555555555557 key: test_accuracy value: [0.61538462 0.5 0.8 0.64 0.68 0.64 0.68 0.52 0.72 0.52 ] mean value: 0.6315384615384616 key: train_accuracy value: [0.74336283 0.74778761 0.74889868 0.77973568 0.80176211 0.7753304 0.71806167 0.72687225 0.75770925 0.78854626] mean value: 0.7588066742037347 key: test_roc_auc value: [0.45714286 0.38571429 0.65 0.475 0.575 0.475 0.65 0.4 0.75 0.475 ] mean value: 0.5292857142857141 key: train_roc_auc value: [0.7145488 0.72565991 0.74304029 0.71208791 0.73418803 0.71770452 0.69871795 0.71257631 0.68162393 0.75940171] mean value: 0.7199549376344957 key: test_jcc value: [0.09090909 0.07142857 0.28571429 0.1 0.2 0.1 0.27272727 0.07692308 0.36363636 0.14285714] mean value: 0.1704195804195804 key: train_jcc value: [0.34090909 0.35227273 0.36666667 0.35064935 0.38356164 0.35443038 0.31914894 0.33333333 0.3125 0.4 ] mean value: 0.35134721285838333 MCC on Blind test: 0.16 MCC on Training: 0.06 Running classifier: 8 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.03171682 0.05332375 0.07818413 0.08152151 0.0595665 0.07796645 0.10740113 0.07640171 0.03545237 0.07910037] mean value: 0.06806347370147706 key: score_time value: [0.01280165 0.01297593 0.02384186 0.02194428 0.02396464 0.01804709 0.04940987 0.014498 0.01460361 0.03149295] mean value: 0.022357988357543945 key: test_mcc value: [-0.17622684 -0.14085904 0. -0.10206207 0. 0.12309149 0. -0.10206207 0.40824829 0. ] mean value: 0.0010129749543951127 key: train_mcc value: [0.81453013 0.7996438 0.81468012 0.82944108 0.79980156 0.78479631 0.76965455 0.81468012 0.76965455 0.78479631] mean value: 0.7981678534540546 key: test_fscore value: [0. 0. 0. 0. 0. 0.25 0. 0. 0.33333333 0. ] mean value: 0.058333333333333334 key: train_fscore value: [0.83116883 0.81578947 0.83116883 0.84615385 0.81578947 0.8 0.78378378 0.83116883 0.78378378 0.8 ] mean value: 0.8138806854596329 key: test_precision value: [0. 0. 0. 0. 0. 0.33333333 0. 0. 1. 0. ] mean value: 0.13333333333333333 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0. 0. 0. 0. 0. 0.2 0. 0. 0.2 0. ] mean value: 0.04 key: train_recall value: [0.71111111 0.68888889 0.71111111 0.73333333 0.68888889 0.66666667 0.64444444 0.71111111 0.64444444 0.66666667] mean value: 0.6866666666666668 key: test_accuracy value: [0.69230769 0.73076923 0.8 0.76 0.8 0.76 0.8 0.76 0.84 0.8 ] mean value: 0.7743076923076923 key: train_accuracy value: [0.94247788 0.9380531 0.94273128 0.94713656 0.93832599 0.9339207 0.92951542 0.94273128 0.92951542 0.9339207 ] mean value: 0.9378328330279521 key: test_roc_auc value: [0.42857143 0.45238095 0.5 0.475 0.5 0.55 0.5 0.475 0.6 0.5 ] mean value: 0.4980952380952381 key: train_roc_auc value: [0.85555556 0.84444444 0.85555556 0.86666667 0.84444444 0.83333333 0.82222222 0.85555556 0.82222222 0.83333333] mean value: 0.8433333333333334 key: test_jcc value: [0. 0. 0. 0. 0. 0.14285714 0. 0. 0.2 0. ] mean value: 0.03428571428571429 key: train_jcc value: [0.71111111 0.68888889 0.71111111 0.73333333 0.68888889 0.66666667 0.64444444 0.71111111 0.64444444 0.66666667] mean value: 0.6866666666666668 MCC on Blind test: 0.14 MCC on Training: 0.0 Running classifier: 9 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.02074218 0.00916958 0.00990152 0.01014209 0.01019001 0.00986981 0.01032257 0.01019955 0.00818396 0.00817442] mean value: 0.010689568519592286 key: score_time value: [0.01473069 0.01080537 0.01076674 0.01158524 0.01120758 0.01120472 0.01086116 0.01140618 0.00974941 0.00952697] mean value: 0.011184406280517579 key: test_mcc value: [-0.14085904 0. 0. 0. 0. -0.14744196 0.22116293 -0.10206207 0. 0. ] mean value: -0.016920013699326993 key: train_mcc value: [0.27725297 0.21600101 0.18535886 0.18535886 0.22652559 0.19360072 0.19360072 0.2701792 0.23089341 0.13597657] mean value: 0.21147478986895654 key: test_fscore value: [0. 0. 0. 0. 0. 0. 0.28571429 0. 0. 0. ] mean value: 0.028571428571428574 key: train_fscore value: [0.28070175 0.21818182 0.12244898 0.12244898 0.16 0.15686275 0.15686275 0.25454545 0.19230769 0.08333333] mean value: 0.1747693502134015 key: test_precision value: [0. 0. 0. 0. 0. 0. 0.5 0. 0. 0. ] mean value: 0.05 key: train_precision value: [0.66666667 0.6 0.75 0.75 0.8 0.66666667 0.66666667 0.7 0.71428571 0.66666667] mean value: 0.6980952380952381 key: test_recall value: [0. 0. 0. 0. 0. 0. 0.2 0. 0. 0. ] mean value: 0.02 key: train_recall value: [0.17777778 0.13333333 0.06666667 0.06666667 0.08888889 0.08888889 0.08888889 0.15555556 0.11111111 0.04444444] mean value: 0.10222222222222224 key: test_accuracy value: [0.73076923 0.80769231 0.8 0.8 0.8 0.72 0.8 0.76 0.8 0.8 ] mean value: 0.7818461538461537 key: train_accuracy value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( [0.81858407 0.80973451 0.81057269 0.81057269 0.81497797 0.81057269 0.81057269 0.81938326 0.81497797 0.8061674 ] mean value: 0.8126115940898989 key: test_roc_auc value: [0.45238095 0.5 0.5 0.5 0.5 0.45 0.575 0.475 0.5 0.5 ] mean value: 0.49523809523809526 key: train_roc_auc value: [0.57783917 0.55561694 0.53058608 0.53058608 0.54169719 0.53894994 0.53894994 0.56953602 0.55006105 0.51947497] mean value: 0.5453297377883012 key: test_jcc value: [0. 0. 0. 0. 0. 0. 0.16666667 0. 0. 0. ] mean value: 0.016666666666666666 key: train_jcc value: [0.16326531 0.12244898 0.06521739 0.06521739 0.08695652 0.08510638 0.08510638 0.14583333 0.10638298 0.04347826] mean value: 0.09690129289458614 MCC on Blind test: 0.14 MCC on Training: -0.02 Running classifier: 10 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.03399348 0.04577971 0.02768826 0.02772427 0.05700684 0.07613826 0.08751512 0.0384531 0.02961373 0.02804899] mean value: 0.04519617557525635 key: score_time value: [0.02231622 0.01189375 0.01187611 0.01196313 0.02490354 0.02390385 0.02206635 0.01201105 0.01184583 0.01187897] mean value: 0.0164658784866333 key: test_mcc value: [-0.03563483 -0.07615914 0.25 0.13363062 0.05455447 0.13363062 0.08574929 0.12309149 0.42146362 -0.18463724] mean value: 0.0905688903083614 key: train_mcc value: [0.84424023 0.84618904 0.79879892 0.82930844 0.85940023 0.8867552 0.83369963 0.75054945 0.85940023 0.91547857] mean value: 0.842381995476293 key: test_fscore value: [0.18181818 0.16666667 0.4 0.33333333 0.22222222 0.33333333 0.30769231 0.25 0.54545455 0. ] mean value: 0.27405205905205904 key: train_fscore value: [0.87356322 0.87640449 0.83333333 0.86046512 0.88636364 0.90697674 0.86666667 0.8 0.88636364 0.93023256] mean value: 0.8720369404104751 key: test_precision value: [0.16666667 0.14285714 0.4 0.28571429 0.25 0.28571429 0.25 0.33333333 0.5 0. ] mean value: 0.26142857142857145 key: train_precision value: [0.9047619 0.88636364 0.8974359 0.90243902 0.90697674 0.95121951 0.86666667 0.8 0.90697674 0.97560976] mean value: 0.8998449886283126 key: test_recall value: [0.2 0.2 0.4 0.4 0.2 0.4 0.4 0.2 0.6 0. ] mean value: 0.30000000000000004 key: train_recall value: [0.84444444 0.86666667 0.77777778 0.82222222 0.86666667 0.86666667 0.86666667 0.8 0.86666667 0.88888889] mean value: 0.8466666666666669 key: test_accuracy value: [0.65384615 0.61538462 0.76 0.68 0.72 0.68 0.64 0.76 0.8 0.68 ] mean value: 0.6989230769230768 key: train_accuracy value: [0.95132743 0.95132743 0.93832599 0.94713656 0.95594714 0.96475771 0.94713656 0.92070485 0.95594714 0.97356828] mean value: 0.9506179096331527 key: test_roc_auc value: [0.48095238 0.45714286 0.625 0.575 0.525 0.575 0.55 0.55 0.725 0.425 ] mean value: 0.5488095238095239 key: train_roc_auc value: [0.9111725 0.91952118 0.87789988 0.9001221 0.92234432 0.92783883 0.91684982 0.87527473 0.92234432 0.94169719] mean value: 0.91150648614737 key: test_jcc value: [0.1 0.09090909 0.25 0.2 0.125 0.2 0.18181818 0.14285714 0.375 0. ] mean value: 0.16655844155844154 key: train_jcc value: [0.7755102 0.78 0.71428571 0.75510204 0.79591837 0.82978723 0.76470588 0.66666667 0.79591837 0.86956522] mean value: 0.7747459694331017 MCC on Blind test: -0.16 MCC on Training: 0.09 Running classifier: 11 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.04026389 0.03489065 0.03226185 0.03426242 0.03471947 0.0347569 0.03413248 0.03500271 0.03004766 0.03528261] mean value: 0.03456206321716308 key: score_time value: [0.01167679 0.01177406 0.01182008 0.01181102 0.01186776 0.01182961 0.01190114 0.01220393 0.01241112 0.01184964] mean value: 0.011914515495300293 key: test_mcc value: [ 0.43469288 -0.09759001 0.58976782 0.12309149 -0.10206207 0.22116293 0.43082022 0. 0.40824829 -0.10206207] mean value: 0.19060694885869922 key: train_mcc value: [0.46720367 0.48796218 0.46745382 0.46745382 0.46745382 0.46745382 0.50840014 0.48823383 0.42428375 0.48811872] mean value: 0.4734017542330912 key: test_fscore value: [0.5 0. 0.57142857 0.25 0. 0.28571429 0.5 0. 0.33333333 0. ] mean value: 0.24404761904761912 key: train_fscore value: [0.47619048 0.48387097 0.45901639 0.45901639 0.45901639 0.45901639 0.50793651 0.48387097 0.42622951 0.5 ] mean value: 0.47141640015780684 key: test_precision value: [0.66666667 0. 1. 0.33333333 0. 0.5 0.66666667 0. 1. 0. ] mean value: 0.41666666666666663 key: train_precision value: [0.83333333 0.88235294 0.875 0.875 0.875 0.875 0.88888889 0.88235294 0.8125 0.84210526] mean value: 0.8641533367733057 key: test_recall value: [0.4 0. 0.4 0.2 0. 0.2 0.4 0. 0.2 0. ] mean value: 0.18 key: train_recall value: [0.33333333 0.33333333 0.31111111 0.31111111 0.31111111 0.31111111 0.35555556 0.33333333 0.28888889 0.35555556] mean value: 0.3244444444444444 key: test_accuracy value: [0.84615385 0.76923077 0.88 0.76 0.76 0.8 0.84 0.8 0.84 0.76 ] mean value: 0.8055384615384616 key: train_accuracy value: [0.8539823 0.85840708 0.85462555 0.85462555 0.85462555 0.85462555 0.86343612 0.85903084 0.84581498 0.85903084] mean value: 0.8558204358504542 key: test_roc_auc value: [0.67619048 0.47619048 0.7 0.55 0.475 0.575 0.675 0.5 0.6 0.475 ] mean value: 0.5702380952380951 key: train_roc_auc value: [0.65837937 0.6611418 0.65006105 0.65006105 0.65006105 0.65006105 0.67228327 0.66117216 0.63620269 0.66953602] mean value: 0.655895951807554 key: test_jcc value: [0.33333333 0. 0.4 0.14285714 0. 0.16666667 0.33333333 0. 0.2 0. ] mean value: 0.1576190476190476 key: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) train_jcc value: [0.3125 0.31914894 0.29787234 0.29787234 0.29787234 0.29787234 0.34042553 0.31914894 0.27083333 0.33333333] mean value: 0.30868794326241134 MCC on Blind test: 0.16 MCC on Training: 0.19 Running classifier: 12 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(cv=3, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(cv=3, random_state=42))]) key: fit_time value: [0.48097944 0.53967714 0.46877623 0.47503448 0.45950031 0.46978259 0.59632874 0.59839082 0.70356178 0.48912382] mean value: 0.5281155347824097 key: score_time value: [0.01462483 0.01440501 0.01412368 0.01443911 0.01437426 0.01424241 0.0143187 0.01390576 0.0135591 0.01298022] mean value: 0.014097309112548828 key: test_mcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_mcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_fscore value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_fscore value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_precision value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_precision value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_recall value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_recall value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_accuracy value: [0.80769231 0.80769231 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 ] mean value: 0.8015384615384615 key: train_accuracy value: [0.80088496 0.80088496 0.80176211 0.80176211 0.80176211 0.80176211 0.80176211 0.80176211 0.80176211 0.80176211] mean value: 0.8015866827803985 key: test_roc_auc value: [0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5] mean value: 0.5 key: train_roc_auc value: [0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5] mean value: 0.5 key: test_jcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_jcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 MCC on Blind test: 0.0 MCC on Training: 0.0 Running classifier: 13 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [1.17974663 0.92163825 1.19651914 1.31567335 1.20766926 1.18887591 0.7405684 1.39054465 0.76307464 0.94360733] mean value: 1.0847917556762696 key: score_time value: [0.01879811 0.0129981 0.01230669 0.01227951 0.01453543 0.01258016 0.01246762 0.01267147 0.01227784 0.01229525] mean value: 0.013321018218994141 key: test_mcc value: [ 0.22537447 -0.14085904 0.05455447 0. 0.42146362 0.25 0.42146362 0.22116293 0.6000992 -0.14744196] mean value: 0.1905817304681802 key: train_mcc value: [0.91539274 0.60760964 0.97219807 0.91855922 0.94399861 0.97228327 0.72225558 0.82944108 0.79772107 0.88759969] mean value: 0.8567058969888952 key: test_fscore value: [0.28571429 0. 0.22222222 0.26666667 0.54545455 0.4 0.54545455 0.28571429 0.66666667 0. ] mean value: 0.3217893217893218 key: train_fscore value: [0.93023256 0.59375 0.97727273 0.93478261 0.95454545 0.97777778 0.77894737 0.84615385 0.82926829 0.90909091] mean value: 0.8731821542779882 key: test_precision value: [0.5 0. 0.25 0.2 0.5 0.4 0.5 0.5 0.75 0. ] mean value: 0.36 key: train_precision value: [0.97560976 1. 1. 0.91489362 0.97674419 0.97777778 0.74 1. 0.91891892 0.93023256] mean value: 0.9434176814001581 key: test_recall value: [0.2 0. 0.2 0.4 0.6 0.4 0.6 0.2 0.6 0. ] mean value: 0.32 key: train_recall value: [0.88888889 0.42222222 0.95555556 0.95555556 0.93333333 0.97777778 0.82222222 0.73333333 0.75555556 0.88888889] mean value: 0.8333333333333333 key: test_accuracy value: [0.80769231 0.73076923 0.72 0.56 0.8 0.76 0.8 0.8 0.88 0.72 ] mean value: 0.7578461538461538 key: train_accuracy value: [0.97345133 0.88495575 0.99118943 0.97356828 0.98237885 0.99118943 0.90748899 0.94713656 0.93832599 0.96475771] mean value: 0.9554442321936767 key: test_roc_auc value: [0.57619048 0.45238095 0.525 0.5 0.725 0.625 0.725 0.575 0.775 0.45 ] mean value: 0.592857142857143 key: train_roc_auc value: [0.94168201 0.71111111 0.97777778 0.96678877 0.96391941 0.98614164 0.87539683 0.86666667 0.86953602 0.93620269] mean value: 0.9095222917046121 key: test_jcc value: [0.16666667 0. 0.125 0.15384615 0.375 0.25 0.375 0.16666667 0.5 0. ] mean value: 0.21121794871794872 key: train_jcc value: [0.86956522 0.42222222 0.95555556 0.87755102 0.91304348 0.95652174 0.63793103 0.73333333 0.70833333 0.83333333] mean value: 0.7907390267451307 MCC on Blind test: -0.04 MCC on Training: 0.19 Running classifier: 14 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.01257014 0.01240063 0.0092485 0.00892043 0.0086062 0.00869894 0.00879264 0.00860548 0.00887132 0.00871396] mean value: 0.00954282283782959 key: score_time value: [0.01158023 0.01135159 0.0088582 0.0085206 0.00834703 0.00839019 0.00840855 0.00857687 0.00837803 0.00834489] mean value: 0.009075617790222168 key: test_mcc value: [-0.09759001 0. 0. 0.05455447 0. 0.22116293 0. 0. 0. 0. ] mean value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) 0.017812739949649048 key: train_mcc value: [0.24766716 0.16211996 0.14466769 0.2701792 0.20459391 0.182109 0.24800027 0.27758684 0.14466769 0.182109 ] mean value: 0.20637007227146986 key: test_fscore value: [0. 0. 0. 0.22222222 0. 0.28571429 0. 0. 0. 0. ] mean value: 0.0507936507936508 key: train_fscore value: [0.25 0.18181818 0.1509434 0.25454545 0.18867925 0.18518519 0.25 0.28070175 0.1509434 0.18518519] mean value: 0.20780017988558205 key: test_precision value: [0. 0. 0. 0.25 0. 0.5 0. 0. 0. 0. ] mean value: 0.075 key: train_precision value: [0.63636364 0.5 0.5 0.7 0.625 0.55555556 0.63636364 0.66666667 0.5 0.55555556] mean value: 0.587550505050505 key: test_recall value: [0. 0. 0. 0.2 0. 0.2 0. 0. 0. 0. ] mean value: 0.04 key: train_recall value: [0.15555556 0.11111111 0.08888889 0.15555556 0.11111111 0.11111111 0.15555556 0.17777778 0.08888889 0.11111111] mean value: 0.12666666666666665 key: test_accuracy value: [0.76923077 0.80769231 0.8 0.72 0.8 0.8 0.8 0.8 0.8 0.8 ] mean value: 0.7896923076923077 key: train_accuracy value: [0.81415929 0.80088496 0.80176211 0.81938326 0.81057269 0.8061674 0.81497797 0.81938326 0.80176211 0.8061674 ] mean value: 0.8095220459241353 key: test_roc_auc value: [0.47619048 0.5 0.5 0.525 0.5 0.575 0.5 0.5 0.5 0.5 ] mean value: 0.5076190476190476 key: train_roc_auc value: [0.56672805 0.5417434 0.53345543 0.56953602 0.5473138 0.54456654 0.56678877 0.57789988 0.53345543 0.54456654] mean value: 0.5526053872462711 key: test_jcc value: [0. 0. 0. 0.125 0. 0.16666667 0. 0. 0. 0. ] mean value: 0.029166666666666664 key: train_jcc value: [0.14285714 0.1 0.08163265 0.14583333 0.10416667 0.10204082 0.14285714 0.16326531 0.08163265 0.10204082] mean value: 0.1166326530612245 MCC on Blind test: 0.08 MCC on Training: 0.02 Running classifier: 15 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.00917912 0.00971627 0.00981522 0.00896621 0.01008296 0.00947332 0.01031446 0.01008511 0.00985551 0.00995302] mean value: 0.00974411964416504 key: score_time value: [0.00881767 0.00871181 0.00944543 0.00909686 0.00935888 0.00910139 0.00939393 0.00957894 0.00905013 0.00952649] mean value: 0.009208154678344727 key: test_mcc value: [-0.09759001 0.00952381 0.40824829 -0.10206207 -0.10206207 0.22116293 0.22116293 -0.10206207 -0.25 -0.14744196] mean value: 0.00588797871547162 key: train_mcc value: [0.25117718 0.30511346 0.26805912 0.30392312 0.2578918 0.2103358 0.32031098 0.22357855 0.20881708 0.24800027] mean value: 0.259720736238719 key: test_fscore value: [0. 0.2 0.33333333 0. 0. 0.28571429 0.28571429 0. 0. 0. ] mean value: 0.11047619047619048 key: train_fscore value: [0.29508197 0.31034483 0.3 0.34920635 0.27586207 0.24137931 0.35483871 0.26666667 0.28125 0.25 ] mean value: 0.29246298996601017 key: test_precision value: [0. 0.2 1. 0. 0. 0.5 0.5 0. 0. 0. ] mean value: 0.22000000000000003 key: train_precision value: [0.5625 0.69230769 0.6 0.61111111 0.61538462 0.53846154 0.64705882 0.53333333 0.47368421 0.63636364] mean value: 0.5910204961017655 key: test_recall value: [0. 0.2 0.2 0. 0. 0.2 0.2 0. 0. 0. ] mean value: 0.08 key: train_recall value: [0.2 0.2 0.2 0.24444444 0.17777778 0.15555556 0.24444444 0.17777778 0.2 0.15555556] mean value: 0.19555555555555557 key: test_accuracy value: [0.76923077 0.69230769 0.84 0.76 0.76 0.8 0.8 0.76 0.6 0.72 ] mean value: 0.7501538461538462 key: train_accuracy value: [0.80973451 0.82300885 0.81497797 0.81938326 0.81497797 0.8061674 0.82378855 0.8061674 0.79735683 0.81497797] mean value: 0.8130540719660052 key: test_roc_auc value: [0.47619048 0.5047619 0.6 0.475 0.475 0.575 0.575 0.475 0.375 0.45 ] mean value: 0.49809523809523804 key: train_roc_auc value: [0.58066298 0.58895028 0.58351648 0.60299145 0.57515263 0.56129426 0.60573871 0.56965812 0.57252747 0.56678877] mean value: 0.5807281147336395 key: test_jcc value: [0. 0.11111111 0.2 0. 0. 0.16666667 0.16666667 0. 0. 0. ] mean value: 0.06444444444444444 key: train_jcc value: [0.17307692 0.18367347 0.17647059 0.21153846 0.16 0.1372549 0.21568627 0.15384615 0.16363636 0.14285714] mean value: 0.1718040279048682 MCC on Blind test: 0.0 MCC on Training: 0.01 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.01054907 0.0157032 0.01486874 0.01539016 0.0147841 0.01566291 0.01689076 0.01551771 0.01523972 0.0162282 ] mean value: 0.01508345603942871 key: score_time value: [0.01442313 0.01179624 0.01168704 0.01192808 0.01213622 0.0118041 0.01189876 0.01202202 0.01170635 0.01210713] mean value: 0.012150907516479492 key: test_mcc value: [ 0.33290015 -0.09759001 0.40824829 0. 0. -0.14744196 0.40824829 0. 0. 0. ] mean value: 0.09043647689085672 key: train_mcc value: [0.56501711 0.3016629 0. 0.18960648 0.26934381 0.47511728 0. 0.48823383 0.18651898 0.30181301] mean value: 0.2777313400793665 key: test_fscore value: [0.44444444 0. 0.33333333 0. 0. 0. 0.33333333 0. 0.33333333 0. ] mean value: 0.14444444444444446 key: train_fscore value: [0.63291139 0.2 0. 0.08510638 0.16326531 0.42105263 0. 0.48387097 0.36885246 0.2 ] mean value: 0.2555059139843512 key: test_precision value: [0.5 0. 1. 0. 0. 0. 1. 0. 0.2 0. ] mean value: 0.27 key: train_precision value: [0.73529412 1. 0. 1. 1. 1. 0. 0.88235294 0.22613065 1. ] mean value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) 0.6843777712089861 key: test_recall value: [0.4 0. 0.2 0. 0. 0. 0.2 0. 1. 0. ] mean value: 0.18 key: train_recall value: [0.55555556 0.11111111 0. 0.04444444 0.08888889 0.26666667 0. 0.33333333 1. 0.11111111] mean value: 0.2511111111111111 key: test_accuracy value: [0.80769231 0.76923077 0.84 0.8 0.8 0.72 0.84 0.8 0.2 0.8 ] mean value: 0.7376923076923078 key: train_accuracy value: [0.87168142 0.82300885 0.80176211 0.81057269 0.81938326 0.85462555 0.80176211 0.85903084 0.3215859 0.82378855] mean value: 0.7787201278702585 key: test_roc_auc value: [0.65238095 0.47619048 0.6 0.5 0.5 0.45 0.6 0.5 0.5 0.5 ] mean value: 0.5278571428571428 key: train_roc_auc value: [0.7529159 0.55555556 0.5 0.52222222 0.54444444 0.63333333 0.5 0.66117216 0.57692308 0.55555556] mean value: 0.5802122248531087 key: test_jcc value: [0.28571429 0. 0.2 0. 0. 0. 0.2 0. 0.2 0. ] mean value: 0.08857142857142856 key: train_jcc value: [0.46296296 0.11111111 0. 0.04444444 0.08888889 0.26666667 0. 0.31914894 0.22613065 0.11111111] mean value: 0.16304647746217296 MCC on Blind test: 0.03 MCC on Training: 0.09 Running classifier: 17 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.02153254 0.0218792 0.02182865 0.02166152 0.02101374 0.02180767 0.02233052 0.02145839 0.02176452 0.02110672] mean value: 0.02163834571838379 key: score_time value: [0.01244664 0.01260757 0.01257634 0.01254106 0.01253653 0.01233983 0.01233506 0.01263952 0.01237774 0.01240492] mean value: 0.012480521202087402 key: test_mcc value: [-0.14085904 0.12923302 0.32732684 0.12309149 -0.14744196 0.12309149 -0.18463724 0. 0.12309149 -0.21821789] mean value: 0.013467820206655632 key: train_mcc value: [0.33120533 0.35855848 0.30181301 0.30181301 0.30181301 0.40862266 0.30181301 0.30181301 0.35872952 0.30181301] mean value: 0.3267994024213786 key: test_fscore value: [0. 0.25 0.44444444 0.25 0. 0.25 0. 0. 0.25 0. ] mean value: 0.14444444444444443 key: train_fscore value: [0.23529412 0.26923077 0.2 0.2 0.2 0.33333333 0.2 0.2 0.26923077 0.2 ] mean value: 0.23070889894419308 key: test_precision value: [0. 0.33333333 0.5 0.33333333 0. 0.33333333 0. 0. 0.33333333 0. ] mean value: 0.1833333333333333 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0. 0.2 0.4 0.2 0. 0.2 0. 0. 0.2 0. ] mean value: 0.12 key: train_recall value: [0.13333333 0.15555556 0.11111111 0.11111111 0.11111111 0.2 0.11111111 0.11111111 0.15555556 0.11111111] mean value: 0.1311111111111111 key: test_accuracy value: [0.73076923 0.76923077 0.8 0.76 0.72 0.76 0.68 0.8 0.76 0.64 ] mean value: 0.7419999999999999 key: train_accuracy value: [0.82743363 0.83185841 0.82378855 0.82378855 0.82378855 0.84140969 0.82378855 0.82378855 0.83259912 0.82378855] mean value: 0.8276032123503956 key: test_roc_auc value: [0.45238095 0.55238095 0.65 0.55 0.45 0.55 0.425 0.5 0.55 0.4 ] mean value: 0.5079761904761905 key: train_roc_auc value: [0.56666667 0.57777778 0.55555556 0.55555556 0.55555556 0.6 0.55555556 0.55555556 0.57777778 0.55555556] mean value: 0.5655555555555555 key: test_jcc value: [0. 0.14285714 0.28571429 0.14285714 0. 0.14285714 0. 0. 0.14285714 0. ] mean value: 0.0857142857142857 key: train_jcc value: [0.13333333 0.15555556 0.11111111 0.11111111 0.11111111 0.2 0.11111111 0.11111111 0.15555556 0.11111111] mean value: 0.1311111111111111 MCC on Blind test: 0.26 MCC on Training: 0.01 Running classifier: 18 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42))]) key: fit_time value: [0.58954358 0.57330108 0.61476517 0.60356712 0.62982774 0.61104655 0.59419775 0.61693287 0.62796783 0.63074112] mean value: 0.6091890811920166 key: score_time value: [0.18245697 0.17063522 0.28685522 0.12449074 0.12912726 0.16067958 0.16272736 0.13594174 0.15922856 0.13041592] mean value: 0.16425585746765137 key: test_mcc value: [ 0. -0.14085904 0.22116293 -0.10206207 0. 0.12309149 0.22116293 -0.10206207 0. 0. ] mean value: 0.022043417175733986 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0. 0. 0.28571429 0. 0. 0.25 0.28571429 0. 0. 0. ] mean value: 0.08214285714285716 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0. 0. 0.5 0. 0. 0.33333333 0.5 0. 0. 0. ] mean value: 0.13333333333333333 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0. 0. 0.2 0. 0. 0.2 0.2 0. 0. 0. ] mean value: 0.06000000000000001 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.80769231 0.73076923 0.8 0.76 0.8 0.76 0.8 0.76 0.8 0.8 ] mean value: 0.7818461538461537 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.5 0.45238095 0.575 0.475 0.5 0.55 0.575 0.475 0.5 0.5 ] mean value: 0.5102380952380952 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0. 0. 0.16666667 0. 0. 0.14285714 0.16666667 0. 0. 0. ] mean value: 0.047619047619047616 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test:/home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) -0.09 MCC on Training: 0.02 Running classifier: 19 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea...age_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42))]) key: fit_time value: [0.94216228 0.95625162 0.97140074 0.93422651 0.95973945 0.95235062 1.01846242 0.94770765 0.96126842 0.98653436] mean value: 0.9630104064941406 key: score_time value: [0.22681999 0.21171403 0.25769806 0.18241882 0.21500897 0.18929553 0.20959401 0.16818023 0.25036478 0.20859146] mean value: 0.21196858882904052 key: test_mcc value: [ 0. -0.09759001 0. 0. 0. -0.10206207 0. 0. 0. 0. ] mean value: -0.019965207991081906 key: train_mcc value: [0.51538185 0.46941646 0.47511728 0.53494254 0.53494254 0.45383596 0.47511728 0.51558901 0.35872952 0.51558901] mean value: 0.4848661459608632 key: test_fscore value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_fscore value: [0.47457627 0.44067797 0.42105263 0.5 0.5 0.39285714 0.42105263 0.47457627 0.26923077 0.47457627] mean value: 0.43685999549068233 key: test_precision value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_precision value: [1. 0.92857143 1. 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9928571428571429 key: test_recall value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_recall value: [0.31111111 0.28888889 0.26666667 0.33333333 0.33333333 0.24444444 0.26666667 0.31111111 0.15555556 0.31111111] mean value: 0.2822222222222222 key: test_accuracy value: [0.80769231 0.76923077 0.8 0.8 0.8 0.76 0.8 0.8 0.8 0.8 ] mean value: 0.7936923076923077 key: train_accuracy value: [0.86283186 0.8539823 0.85462555 0.86784141 0.86784141 0.85022026 0.85462555 0.86343612 0.83259912 0.86343612] mean value: 0.8571439709952828 key: test_roc_auc value: [0.5 0.47619048 0.5 0.5 0.5 0.475 0.5 0.5 0.5 0.5 ] mean value: 0.49511904761904757 key: train_roc_auc value: [0.65555556 0.64168201 0.63333333 0.66666667 0.66666667 0.62222222 0.63333333 0.65555556 0.57777778 0.65555556] mean value: 0.6408348680171885 key: test_jcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_jcc value: [0.31111111 0.2826087 0.26666667 0.33333333 0.33333333 0.24444444 0.26666667 0.31111111 0.15555556 0.31111111] mean value: 0.2815942028985507 MCC on Blind test: 0.0 MCC on Training: -0.02 Running classifier: 20 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.02388453 0.03486037 0.03506827 0.03531528 0.03768134 0.03516865 0.03568077 0.0347147 0.0360353 0.02878547] mean value: 0.03371946811676026 key: score_time value: [0.0207355 0.02144909 0.02189803 0.02181482 0.0286715 0.0238781 0.016747 0.02471733 0.02002335 0.02072072] mean value: 0.02206554412841797 key: test_mcc value: [ 0.22537447 -0.14085904 0.58976782 -0.18463724 -0.14744196 0.35634832 0.32732684 0. 0.73854895 -0.14744196] mean value: 0.16169862050935307 key: train_mcc value: [0.63842213 0.68838405 0.61961972 0.61961972 0.60203353 0.6386524 0.63690155 0.60203353 0.56583118 0.68860178] mean value: 0.6300099598958251 key: test_fscore value: [0.28571429 0. 0.57142857 0. 0. 0.5 0.44444444 0. 0.75 0. ] mean value: 0.25515873015873014 key: train_fscore value: [0.64705882 0.70422535 0.63768116 0.63768116 0.61764706 0.64705882 0.65714286 0.61764706 0.57575758 0.70422535] mean value: 0.6446125220672247 key: test_precision value: [0.5 0. 1. 0. 0. 0.42857143 0.5 0. 1. 0. ] mean value: 0.34285714285714286 key: train_precision value: [0.95652174 0.96153846 0.91666667 0.91666667 0.91304348 0.95652174 0.92 0.91304348 0.9047619 0.96153846] mean value: 0.932030259595477 key: test_recall value: [0.2 0. 0.4 0. 0. 0.6 0.4 0. 0.6 0. ] mean value: 0.22000000000000003 key: train_recall value: [0.48888889 0.55555556 0.48888889 0.48888889 0.46666667 0.48888889 0.51111111 0.46666667 0.42222222 0.55555556] mean value: 0.4933333333333333 key: test_accuracy value: [0.80769231 0.73076923 0.88 0.68 0.72 0.76 0.8 0.8 0.92 0.72 ] mean value: 0.7818461538461539 key: train_accuracy value: [0.89380531 0.90707965 0.88986784 0.88986784 0.88546256 0.89427313 0.89427313 0.88546256 0.87665198 0.90748899] mean value: 0.8924232973373358 key: test_roc_auc value: [0.57619048 0.45238095 0.7 0.425 0.45 0.7 0.65 0.5 0.8 0.45 ] mean value: 0.5703571428571428 key: train_roc_auc value: [0.74168201 0.77501535 0.73894994 0.73894994 0.72783883 0.74169719 0.75006105 0.72783883 0.70561661 0.77503053] mean value: 0.7422680266326676 key: test_jcc value: [0.16666667 0. 0.4 0. 0. 0.33333333 0.28571429 0. 0.6 0. ] mean value: 0.17857142857142855 key: train_jcc value: [0.47826087 0.54347826 0.46808511 0.46808511 0.44680851 0.47826087 0.4893617 0.44680851 0.40425532 0.54347826] mean value: 0.4766882516188714 MCC on Blind test: 0.01 MCC on Training: 0.16 Running classifier: 21 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=3) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=3))]) key: fit_time value: [0.1299758 0.12892032 0.0996573 0.09483266 0.06575632 0.05797219 0.09692955 0.10306191 0.10117984 0.08941627] mean value: 0.09677021503448487 key: score_time value: [0.03028917 0.01712346 0.01729584 0.01961374 0.01186633 0.01700926 0.02248502 0.02199793 0.02086115 0.02387428] mean value: 0.020241618156433105 key: test_mcc value: [-0.09759001 -0.09759001 0.40824829 0.22116293 0. 0.22116293 0.22116293 0. 0. 0. ] mean value: 0.08765570785711935 key: train_mcc value: [0.37764415 0.35065295 0.32402527 0.37635206 0.40237704 0.35199424 0.35091811 0.40055981 0.29536583 0.35091811] mean value: 0.3580807583560495 key: test_fscore value: [0. 0. 0.33333333 0.28571429 0. 0.28571429 0.28571429 0. 0. 0. ] mean value: 0.11904761904761907 key: train_fscore value: [0.32727273 0.32142857 0.29090909 0.35087719 0.35714286 0.2962963 0.32142857 0.37931034 0.25925926 0.32142857] mean value: 0.3225353482975987 key: test_precision value: [0. 0. 1. 0.5 0. 0.5 0.5 0. 0. 0. ] mean value: 0.25 key: train_precision value: [0.9 0.81818182 0.8 0.83333333 0.90909091 0.88888889 0.81818182 0.84615385 0.77777778 0.81818182] mean value: 0.8409790209790209 key: test_recall value: [0. 0. 0.2 0.2 0. 0.2 0.2 0. 0. 0. ] mean value: 0.08 key: train_recall value: [0.2 0.2 0.17777778 0.22222222 0.22222222 0.17777778 0.2 0.24444444 0.15555556 0.2 ] mean value: 0.19999999999999998 key: test_accuracy value: [0.76923077 0.76923077 0.84 0.8 0.8 0.8 0.8 0.8 0.8 0.8 ] mean value: 0.7978461538461539 key: train_accuracy value: [0.83628319 0.83185841 0.82819383 0.83700441 0.84140969 0.83259912 0.83259912 0.84140969 0.82378855 0.83259912] mean value: 0.833774511714943 key: test_roc_auc value: [0.47619048 0.47619048 0.6 0.575 0.5 0.575 0.575 0.5 0.5 0.5 ] mean value: 0.5277380952380952 key: train_roc_auc value: [0.59723757 0.59447514 0.58339438 0.60561661 0.60836386 0.58614164 0.59450549 0.61672772 0.57228327 0.59450549] mean value: 0.5953251168720782 key: test_jcc value: [0. 0. 0.2 0.16666667 0. 0.16666667 0.16666667 0. 0. 0. ] mean value: 0.06999999999999999 key: train_jcc value: [0.19565217 0.19148936 0.17021277 0.21276596 0.2173913 0.17391304 0.19148936 0.23404255 0.14893617 0.19148936] mean value: 0.1927382053654024 MCC on Blind test: -0.06 MCC on Training: 0.09 Running classifier: 22 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.02468204 0.01139855 0.01092529 0.0108757 0.01073718 0.01084232 0.01096702 0.01150489 0.01150751 0.0107789 ] mean value: 0.012421941757202149 key: score_time value: [0.01168227 0.00941634 0.00909734 0.00908685 0.00917959 0.0090282 0.0092535 0.00916123 0.00947881 0.00911212] mean value: 0.009449625015258789 key: test_mcc value: [ 0. 0. 0. 0. 0. -0.10206207 0. 0. 0. 0. ] mean value: -0.010206207261596576 key: train_mcc value: [0. 0. 0. 0.18960648 0. 0.18960648 0. 0. 0. 0.13377508] mean value: 0.05129880395727841 key: test_fscore value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_fscore value: [0. 0. 0. 0.08510638 0. 0.08510638 0. 0. 0. 0.04347826] mean value: 0.021369102682701202 key: test_precision value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_precision value: [0. 0. 0. 1. 0. 1. 0. 0. 0. 1.] mean value: 0.3 key: test_recall value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_recall value: [0. 0. 0. 0.04444444 0. 0.04444444 0. 0. 0. 0.02222222] mean value: 0.011111111111111112 key: test_accuracy value: [0.80769231 0.80769231 0.8 0.8 0.8 0.76 0.8 0.8 0.8 0.8 ] mean value: 0.7975384615384615 key: train_accuracy value: [0.80088496 0.80088496 0.80176211 0.81057269 0.80176211 0.81057269 0.80176211 0.80176211 0.80176211 0.8061674 ] mean value: 0.8037893259522045 key: test_roc_auc value: [0.5 0.5 0.5 0.5 0.5 0.475 0.5 0.5 0.5 0.5 ] mean value: 0.49749999999999994 key: train_roc_auc value: [0.5 0.5 0.5 0.52222222 0.5 0.52222222 0.5 0.5 0.5 0.51111111] mean value: 0.5055555555555555 key: test_jcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_jcc value: [0. 0. 0. 0.04444444 0. 0.04444444 0. 0. 0. 0.02222222] mean value: 0.011111111111111112 MCC on Blind test: 0.0 MCC on Training: -0.01 Running classifier: 23 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.0118506 0.01507258 0.01450086 0.01951766 0.01839757 0.01636815 0.01686859 0.0169642 0.01555514 0.01576972] mean value: 0.016086506843566894 key: score_time value: /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:463: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_CV['source_data'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:490: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_BT['source_data'] = 'BT' [0.00990438 0.0114193 0.01148319 0.0122807 0.01262808 0.01209569 0.01218438 0.01207709 0.01211405 0.01207614] mean value: 0.011826300621032714 key: test_mcc value: [ 0.06023386 -0.09759001 0.40824829 -0.14744196 0. -0.10206207 0.04166667 0.32732684 0.42146362 -0.10206207] mean value: 0.0809783159208282 key: train_mcc value: [0.40606448 0.58385195 0.43171742 0.57219525 0.35872952 0.38437256 0.62069712 0.62077138 0.54476539 0.54774763] mean value: 0.5070912697359452 key: test_fscore value: [0.31578947 0. 0.33333333 0. 0. 0. 0.28571429 0.44444444 0.54545455 0. ] mean value: 0.19247360826308196 key: train_fscore value: [0.50285714 0.59701493 0.36363636 0.5483871 0.26923077 0.30188679 0.68907563 0.69902913 0.6407767 0.57971014] mean value: 0.5191604690746788 key: test_precision value: [0.21428571 0. 1. 0. 0. 0. 0.22222222 0.5 0.5 0. ] mean value: 0.24365079365079362 key: train_precision value: [0.33846154 0.90909091 1. 1. 1. 1. 0.55405405 0.62068966 0.56896552 0.83333333] mean value: 0.7824595007353627 key: test_recall value: [0.6 0. 0.2 0. 0. 0. 0.4 0.4 0.6 0. ] mean value: 0.22000000000000003 key: train_recall value: [0.97777778 0.44444444 0.22222222 0.37777778 0.15555556 0.17777778 0.91111111 0.8 0.73333333 0.44444444] mean value: 0.5244444444444445 key: test_accuracy value: [0.5 0.76923077 0.84 0.72 0.8 0.76 0.6 0.8 0.8 0.76 ] mean value: 0.7349230769230769 key: train_accuracy value: [0.61504425 0.88053097 0.84581498 0.87665198 0.83259912 0.83700441 0.83700441 0.86343612 0.83700441 0.8722467 ] mean value: 0.8297337335776384 key: test_roc_auc value: [0.53809524 0.47619048 0.6 0.45 0.5 0.475 0.525 0.65 0.725 0.475 ] mean value: 0.5414285714285713 key: train_roc_auc value: [0.75131983 0.71669736 0.61111111 0.68888889 0.57777778 0.58888889 0.86489621 0.83956044 0.79798535 0.71123321] mean value: 0.7148359068801058 key: test_jcc value: [0.1875 0. 0.2 0. 0. 0. 0.16666667 0.28571429 0.375 0. ] mean value: 0.12148809523809523 key: train_jcc value: [0.33587786 0.42553191 0.22222222 0.37777778 0.15555556 0.17777778 0.52564103 0.53731343 0.47142857 0.40816327] mean value: 0.3637289406033911 MCC on Blind test: -0.11 MCC on Training: 0.08 Running classifier: 24 Model_name: XGBoost Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.09320521 0.06219053 0.07930779 0.06354189 0.07376099 0.06270576 0.06365252 0.07087994 0.06882834 0.07184935] mean value: 0.07099223136901855 key: score_time value: [0.01143551 0.01159334 0.01078296 0.0104146 0.01091862 0.01058149 0.01028132 0.01129556 0.01126003 0.0106554 ] mean value: 0.010921883583068847 key: test_mcc value: [ 0.06241878 0.33290015 0.32732684 -0.10206207 -0.14744196 0.22116293 0.25 0.40824829 0.58976782 0.40824829] mean value: 0.23505690761866135 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.22222222 0.44444444 0.44444444 0. 0. 0.28571429 0.4 0.33333333 0.57142857 0.33333333] mean value: 0.3034920634920636 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.25 0.5 0.5 0. 0. 0.5 0.4 1. 1. 1. ] mean value: 0.515 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.2 0.4 0.4 0. 0. 0.2 0.4 0.2 0.4 0.2] mean value: 0.24000000000000005 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.73076923 0.80769231 0.8 0.76 0.72 0.8 0.76 0.84 0.88 0.84 ] mean value: 0.7938461538461538 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.52857143 0.65238095 0.65 0.475 0.45 0.575 0.625 0.6 0.7 0.6 ] mean value: 0.5855952380952381 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.125 0.28571429 0.28571429 0. 0. 0.16666667 0.25 0.2 0.4 0.2 ] mean value: 0.19130952380952382 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.34 MCC on Training: 0.24 Extracting tts_split_name: 80_20 Total cols in each df: CV df: 8 metaDF: 17 Adding column: Model_name Total cols in bts df: BT_df: 8 First proceeding to rowbind CV and BT dfs: Final output should have: 25 columns Combinig 2 using pd.concat by row ~ rowbind Checking Dims of df to combine: Dim of CV: (24, 8) Dim of BT: (24, 8) 8 Number of Common columns: 8 These are: ['source_data', 'Precision', 'JCC', 'MCC', 'Recall', 'Accuracy', 'F1', 'ROC_AUC'] Concatenating dfs with different resampling methods [WF]: Split type: 80_20 No. of dfs combining: 2 PASS: 2 dfs successfully combined nrows in combined_df_wf: 48 ncols in combined_df_wf: 8 PASS: proceeding to merge metadata with CV and BT dfs Adding column: Model_name ========================================================= SUCCESS: Ran multiple classifiers ======================================================= ============================================================== Running several classification models (n): 24 List of models: ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('Gaussian NB', GaussianNB()) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('K-Nearest Neighbors', KNeighborsClassifier()) ('LDA', LinearDiscriminantAnalysis()) ('Logistic Regression', LogisticRegression(random_state=42)) [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... 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Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... âgë’$”Œnumpy”Œdtype”“”Œi8”‰ˆ‡”R”(KŒ<”NNNJÿÿÿÿJÿÿÿÿKt”bK …”ŒC”t”R”.”…”R”Kdt”}”(h:KŒ check_input”ˆu‡”ahŒThreadingBackend”“”)”}”(Œ nesting_level”KŒinner_max_num_threads”NubN†”N}”t”R”sbŒargs”)Œkwargs”}”Œ loky_pickler”Œ cloudpickle”uBuilding estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... ubN†”N}”t”R”sbŒargs”)Œkwargs”}”Œ loky_pickler”Œ cloudpickle”u[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished ('Logistic RegressionCV', LogisticRegressionCV(cv=3, random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Multinomial', MultinomialNB()) ('Naive Bayes', BernoulliNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=12, random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Random Forest', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42)) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=3)) ('SVC', SVC(random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=12, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ================================================================ Running classifier: 1 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.20304871 0.20563412 0.2173481 0.22470546 0.21390939 0.21650124 0.20941114 0.20239806 0.2050581 0.21255255] mean value: 0.21105668544769288 key: score_time value: [0.01537061 0.01609182 0.01607323 0.0174396 0.01681781 0.0202086 0.01543808 0.01656675 0.01551127 0.016541 ] mean value: 0.01660587787628174 key: test_mcc value: [0.41428571 0.76500781 0.7565654 0.46300848 0.7 0.70352647 0.60302269 0.80403025 0.65081403 0.85972695] mean value: 0.6719987792826986 key: train_mcc value: [0.98348613 0.98360606 0.98904035 0.98904035 0.98901099 0.97265962 0.97289468 0.98353133 0.96703297 0.98901099] mean value: 0.9819313471972828 key: test_fscore value: [0.7 0.88372093 0.88372093 0.74418605 0.85 0.84210526 0.80952381 0.9047619 0.82051282 0.93023256] mean value: 0.8368764263072709 key: train_fscore value: [0.99173554 0.99168975 0.99444444 0.99444444 0.99450549 0.98614958 0.98607242 0.99173554 0.98351648 0.99450549] mean value: 0.9908799194374911 key: test_precision value: [0.7 0.82608696 0.86363636 0.72727273 0.85 0.88888889 0.77272727 0.86363636 0.84210526 0.86956522] mean value: 0.8203919053232553 key: train_precision value: [0.99447514 1. 1. 1. 0.99450549 0.99441341 1. 0.99447514 0.98351648 0.99450549] mean value: 0.9955891156591795 key: test_recall value: [0.7 0.95 0.9047619 0.76190476 0.85 0.8 0.85 0.95 0.8 1. ] mean value: 0.8566666666666667 key: train_recall value: [0.98901099 0.98351648 0.98895028 0.98895028 0.99450549 0.97802198 0.97252747 0.98901099 0.98351648 0.99450549] mean value: 0.9862515937101574 key: test_accuracy value: [0.70731707 0.87804878 0.87804878 0.73170732 0.85 0.85 0.8 0.9 0.825 0.925 ] mean value: 0.8345121951219513 key: train_accuracy value: [0.99173554 0.99173554 0.99449036 0.99449036 0.99450549 0.98626374 0.98626374 0.99175824 0.98351648 0.99450549] mean value: 0.9909264977446796 key: test_roc_auc value: [0.70714286 0.8797619 0.87738095 0.73095238 0.85 0.85 0.8 0.9 0.825 0.925 ] mean value: 0.8345238095238094 key: train_roc_auc value: [0.99174306 0.99175824 0.99447514 0.99447514 0.99450549 0.98626374 0.98626374 0.99175824 0.98351648 0.99450549] mean value: 0.990926476838079 key: test_jcc value: [0.53846154 0.79166667 0.79166667 0.59259259 0.73913043 0.72727273 0.68 0.82608696 0.69565217 0.86956522] mean value: 0.7252094974268888 key: train_jcc value: [0.98360656 0.98351648 0.98895028 0.98895028 0.98907104 0.9726776 0.97252747 0.98360656 0.96756757 0.98907104] mean value: 0.9819544862982956 MCC on Blind test: 0.18 MCC on Training: 0.67 Running classifier: 2 Model_name: Bagging Classifier Model func: BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3))]) key: fit_time value: [0.21035862 0.26446915 0.26723695 0.26302624 0.26365042 0.2290771 0.22819257 0.27566195 0.28134322 0.27003908] mean value: 0.25530552864074707 key: score_time value: [0.04471874 0.08038807 0.0639112 0.07343554 0.03952074 0.04846168 0.04078269 0.0633502 0.05344629 0.06905985] mean value: 0.05770750045776367 key: test_mcc value: [0.60952381 0.72229808 0.90238095 0.8047619 0.75858261 0.56803756 0.8 0.90453403 0.65743826 0.95118973] mean value: 0.7678746935759748 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.8 0.86363636 0.95238095 0.9047619 0.88372093 0.74285714 0.9 0.95238095 0.81081081 0.97560976] mean value: 0.8786158813158245 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.8 0.79166667 0.95238095 0.9047619 0.82608696 0.86666667 0.9 0.90909091 0.88235294 0.95238095] mean value: 0.8785387949646262 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.8 0.95 0.95238095 0.9047619 0.95 0.65 0.9 1. 0.75 1. ] mean value: 0.8857142857142858 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.80487805 0.85365854 0.95121951 0.90243902 0.875 0.775 0.9 0.95 0.825 0.975 ] mean value: 0.881219512195122 key: train_accuracy [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.2s remaining: 2.3s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.2s remaining: 2.5s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.3s remaining: 2.5s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.2s remaining: 2.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.3s remaining: 0.4s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.3s remaining: 0.4s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.3s remaining: 2.6s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.3s remaining: 0.4s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.3s remaining: 0.4s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.3s remaining: 2.6s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.3s remaining: 0.4s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.3s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.3s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.4s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.3s remaining: 0.4s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.3s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.4s remaining: 2.7s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.4s remaining: 2.7s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.4s remaining: 2.8s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.4s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.4s remaining: 2.7s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.4s remaining: 0.5s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.4s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.4s remaining: 0.5s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.4s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.4s remaining: 0.5s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.4s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.4s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.4s remaining: 0.5s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.5s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.8047619 0.85595238 0.95119048 0.90238095 0.875 0.775 0.9 0.95 0.825 0.975 ] mean value: 0.8814285714285715 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.66666667 0.76 0.90909091 0.82608696 0.79166667 0.59090909 0.81818182 0.90909091 0.68181818 0.95238095] mean value: 0.7905892151326934 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.38 MCC on Training: 0.77 Running classifier: 3 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.02486968 0.02595496 0.02689552 0.02776217 0.02662015 0.02712345 0.02917695 0.02577209 0.02838159 0.02632833] mean value: 0.026888489723205566 key: score_time value: [0.0091846 0.00902772 0.00904655 0.00888395 0.00890088 0.00918365 0.00904822 0.00913286 0.0090127 0.00893927] mean value: 0.009036040306091309 key: test_mcc value: [0.51190476 0.56836003 0.90649828 0.6133669 0.60302269 0.45514956 0.55629391 0.70352647 0.60302269 0.71443451] mean value: 0.6235579807682194 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.75 0.79069767 0.95454545 0.8 0.78947368 0.7027027 0.79069767 0.85714286 0.80952381 0.86363636] mean value: 0.8108420220598923 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.75 0.73913043 0.91304348 0.84210526 0.83333333 0.76470588 0.73913043 0.81818182 0.77272727 0.79166667] mean value: 0.7964024584246013 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.75 0.85 1. 0.76190476 0.75 0.65 0.85 0.9 0.85 0.95 ] mean value: 0.8311904761904761 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.75609756 0.7804878 0.95121951 0.80487805 0.8 0.725 0.775 0.85 0.8 0.85 ] mean value: 0.8092682926829269 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.75595238 0.78214286 0.95 0.80595238 0.8 0.725 0.775 0.85 0.8 0.85 ] mean value: 0.809404761904762 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.6 0.65384615 0.91304348 0.66666667 0.65217391 0.54166667 0.65384615 0.75 0.68 0.76 ] mean value: 0.6871243032329988 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.18 MCC on Training: 0.62 Running classifier: 4 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.00949526 0.00923181 0.01000953 0.00947404 0.00945616 0.00943995 0.01017809 0.01064253 0.01027513 0.00950432] mean value: 0.009770679473876952 key: score_time value: [0.00837421 0.00859642 0.00901031 0.0084393 0.00856423 0.00867438 0.00877905 0.00922179 0.00883031 0.00871229] mean value: 0.00872023105621338 key: test_mcc value: [0.35038478 0.27338837 0.65871309 0.52052166 0.75858261 0.55068879 0.65081403 0.5 0.35043832 0.61237244] mean value: 0.5225904086778352 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.70833333 0.65116279 0.8372093 0.66666667 0.88372093 0.76923077 0.82926829 0.75 0.66666667 0.81818182] mean value: 0.7580440570017996 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.60714286 0.60869565 0.81818182 0.91666667 0.82608696 0.78947368 0.80952381 0.75 0.68421053 0.75 ] mean value: 0.755998197073712 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.85 0.7 0.85714286 0.52380952 0.95 0.75 0.85 0.75 0.65 0.9 ] mean value: 0.7780952380952382 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.65853659 0.63414634 0.82926829 0.73170732 0.875 0.775 0.825 0.75 0.675 0.8 ] mean value: 0.7553658536585365 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.66309524 0.63571429 0.82857143 0.73690476 0.875 0.775 0.825 0.75 0.675 0.8 ] mean value: 0.7564285714285715 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.5483871 0.48275862 0.72 0.5 0.79166667 0.625 0.70833333 0.6 0.5 0.69230769] mean value: 0.616845340977154 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.05 MCC on Training: 0.52 Running classifier: 5 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.11048698 0.11075926 0.11626577 0.11808038 0.11810637 0.11795568 0.11481619 0.11742496 0.10988545 0.11081529] mean value: 0.11445963382720947 key: score_time value: [0.01747966 0.01882672 0.0185554 0.01851749 0.01789236 0.01843357 0.01859117 0.01770186 0.01751304 0.01760697] mean value: 0.018111824989318848 key: test_mcc value: [0.75714286 0.71121921 0.80907152 0.63994524 0.75093926 0.65081403 0.8510645 0.95118973 0.61237244 0.9 ] mean value: 0.7633758777877204 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.87804878 0.85714286 0.9 0.77777778 0.87179487 0.82051282 0.92307692 0.97560976 0.77777778 0.95 ] mean value: 0.8731741564668394 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.85714286 0.81818182 0.94736842 0.93333333 0.89473684 0.84210526 0.94736842 0.95238095 0.875 0.95 ] mean value: 0.901761790840738 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.9 0.9 0.85714286 0.66666667 0.85 0.8 0.9 1. 0.7 0.95 ] mean value: 0.8523809523809524 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.87804878 0.85365854 0.90243902 0.80487805 0.875 0.825 0.925 0.975 0.8 0.95 ] mean value: 0.8789024390243902 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.87857143 0.8547619 0.90357143 0.80833333 0.875 0.825 0.925 0.975 0.8 0.95 ] mean value: 0.8795238095238094 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.7826087 0.75 0.81818182 0.63636364 0.77272727 0.69565217 0.85714286 0.95238095 0.63636364 0.9047619 ] mean value: 0.7806182947487296 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.06 MCC on Training: 0.76 Running classifier: 6 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.76514482 0.75020051 0.75181222 0.76237273 0.76621389 0.76209402 0.75726461 0.75694871 0.76831603 0.748492 ] mean value: 0.7588859558105469 key: score_time value: [0.00907826 0.00919509 0.00943756 0.00950241 0.01011276 0.0100224 0.00937128 0.00914836 0.00912046 0.00986338] mean value: 0.009485197067260743 key: test_mcc value: [0.60952381 0.76500781 0.8547619 0.76500781 0.80403025 0.70352647 0.70352647 0.95118973 0.71443451 0.85972695] mean value: 0.7730735715325149 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.8 0.88372093 0.92682927 0.87179487 0.9047619 0.84210526 0.85714286 0.97560976 0.83333333 0.93023256] mean value: 0.8825530742953198 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.8 0.82608696 0.95 0.94444444 0.86363636 0.88888889 0.81818182 0.95238095 0.9375 0.86956522] mean value: 0.8850684641445511 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.8 0.95 0.9047619 0.80952381 0.95 0.8 0.9 1. 0.75 1. ] mean value: 0.8864285714285713 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.80487805 0.87804878 0.92682927 0.87804878 0.9 0.85 0.85 0.975 0.85 0.925 ] mean value: 0.8837804878048781 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.8047619 0.8797619 0.92738095 0.8797619 0.9 0.85 0.85 0.975 0.85 0.925 ] mean value: 0.8841666666666667 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.66666667 0.79166667 0.86363636 0.77272727 0.82608696 0.72727273 0.75 0.95238095 0.71428571 0.86956522] mean value: 0.7934288537549408 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.23 MCC on Training: 0.77 Running classifier: 7 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.0097456 0.00988746 0.01028395 0.00965643 0.00977373 0.00986624 0.00988603 0.00986218 0.00982857 0.00994635] mean value: 0.009873652458190918 key: score_time value: [0.00918984 0.00919414 0.0092566 0.00924325 0.00920177 0.00916171 0.00926661 0.00930548 0.00917745 0.00923681] mean value: 0.009223365783691406 key: test_mcc value: [0.17142857 0.33603302 0.7633652 0.36666667 0.20100756 0.56803756 0.35400522 0.37363236 0.20412415 0.46188022] mean value: 0.3800180514745349 key: train_mcc value: [0.53583618 0.4809694 0.45255019 0.51845035 0.50478494 0.48708819 0.49815837 0.50922856 0.48646263 0.49963004] mean value: 0.49731588424391376 key: test_fscore value: [0.58536585 0.69565217 0.88888889 0.68292683 0.61904762 0.74285714 0.69767442 0.72340426 0.55555556 0.76 ] mean value: 0.695137273711288 key: train_fscore value: [0.78036176 0.75578406 0.73958333 0.76963351 0.76606684 0.75647668 0.76165803 0.76683938 0.75520833 0.76410256] mean value: 0.7615714488735756 key: test_precision value: [0.57142857 0.61538462 0.83333333 0.7 0.59090909 0.86666667 0.65217391 0.62962963 0.625 0.63333333] mean value: 0.6717859153728719 key: train_precision value: [0.73658537 0.71014493 0.69950739 0.73134328 0.71980676 0.71568627 0.72058824 0.7254902 0.71782178 0.71634615] mean value: 0.719332037132629 key: test_recall value: [0.6 0.8 0.95238095 0.66666667 0.65 0.65 0.75 0.85 0.5 0.95 ] mean value: 0.736904761904762 key: train_recall value: [0.82967033 0.80769231 0.78453039 0.8121547 0.81868132 0.8021978 0.80769231 0.81318681 0.7967033 0.81868132] mean value: 0.8091190577378423 key: test_accuracy value: [0.58536585 0.65853659 0.87804878 0.68292683 0.6 0.775 0.675 0.675 0.6 0.7 ] mean value: 0.6829878048780487 key: train_accuracy value: [0.76584022 0.73829201 0.72451791 0.75757576 0.75 0.74175824 0.74725275 0.75274725 0.74175824 0.74725275] mean value: 0.7466995126086035 key: test_roc_auc value: [0.58571429 0.66190476 0.87619048 0.68333333 0.6 0.775 0.675 0.675 0.6 0.7 ] mean value: 0.6832142857142857 key: train_roc_auc value: [0.76566389 0.7381003 0.72468278 0.7577257 0.75 0.74175824 0.74725275 0.75274725 0.74175824 0.74725275] mean value: 0.7466941897881124 key: test_jcc value: [0.4137931 0.53333333 0.8 0.51851852 0.44827586 0.59090909 0.53571429 0.56666667 0.38461538 0.61290323] mean value: 0.5404729471080973 key: train_jcc value: [0.63983051 0.60743802 0.58677686 0.62553191 0.62083333 0.60833333 0.61506276 0.62184874 0.60669456 0.61825726] mean value: 0.6150607289150237 MCC on Blind test: 0.02 MCC on Training: 0.38 Running classifier: 8 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.10161805 0.11068344 0.10917783 0.10654569 0.11384416 0.14290977 0.10133576 0.0875423 0.08646274 0.11286426] mean value: 0.1072983980178833 key: score_time value: [0.02255797 0.0221982 0.02182746 0.02163124 0.02150631 0.02165937 0.01374078 0.02182174 0.02158689 0.02156186] mean value: 0.021009182929992674 key: test_mcc value: [0.56836003 0.58066054 0.85441771 0.41766229 0.73379939 0.65081403 0.55068879 0.73379939 0.5 0.75858261] mean value: 0.6348784766824621 key: train_mcc value: [0.96156183 0.96156183 0.97796127 0.95598191 0.97254215 0.96166912 0.94511201 0.95610169 0.96190152 0.97254215] mean value: 0.9626935481503145 key: test_fscore value: [0.79069767 0.8 0.93023256 0.7 0.86956522 0.82051282 0.7804878 0.86956522 0.75 0.88372093] mean value: 0.8194782222964175 key: train_fscore value: [0.98092643 0.98092643 0.98895028 0.97802198 0.9862259 0.98092643 0.9726776 0.97814208 0.98102981 0.98630137] mean value: 0.9814128293427276 key: test_precision value: [0.73913043 0.72 0.90909091 0.73684211 0.76923077 0.84210526 0.76190476 0.76923077 0.75 0.82608696] mean value: 0.782362196918261 key: train_precision value: [0.97297297 0.97297297 0.98895028 0.9726776 0.98895028 0.97297297 0.9673913 0.97282609 0.96791444 0.98360656] mean value: 0.9761235454217594 key: test_recall value: [0.85 0.9 0.95238095 0.66666667 1. 0.8 0.8 1. 0.75 0.95 ] mean value: 0.866904761904762 key: train_recall value: [0.98901099 0.98901099 0.98895028 0.98342541 0.98351648 0.98901099 0.97802198 0.98351648 0.99450549 0.98901099] mean value: 0.9867980086212131 key: test_accuracy value: [0.7804878 0.7804878 0.92682927 0.70731707 0.85 0.825 0.775 0.85 0.75 0.875 ] mean value: 0.8120121951219513 key: train_accuracy value: [0.98071625 0.98071625 0.98898072 0.97796143 0.98626374 0.98076923 0.97252747 0.97802198 0.98076923 0.98626374] mean value: 0.9812990040262768 key: test_roc_auc value: [0.78214286 0.78333333 0.92619048 0.70833333 0.85 0.825 0.775 0.85 0.75 0.875 ] mean value: 0.8125 key: train_roc_auc value: [0.98069334 0.98069334 0.98898063 0.97797644 0.98626374 0.98076923 0.97252747 0.97802198 0.98076923 0.98626374] mean value: 0.9812959140307205 key: test_jcc value: [0.65384615 0.66666667 0.86956522 0.53846154 0.76923077 0.69565217 0.64 0.76923077 0.6 0.79166667] mean value: 0.6994319955406911 key: train_jcc value: [0.96256684 0.96256684 0.97814208 0.95698925 0.97282609 0.96256684 0.94680851 0.95721925 0.96276596 0.97297297] mean value: 0.9635424637925419 MCC on Blind test: 0.1 MCC on Training: 0.63 Running classifier: 9 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.01080108 0.010463 0.01038146 0.00999618 0.00997353 0.00993681 0.01005006 0.00994825 0.00980473 0.00976944] mean value: 0.010112452507019042 key: score_time value: [0.01236892 0.01343107 0.01274943 0.01229 0.01226997 0.01229644 0.01483655 0.01212192 0.01389742 0.0172658 ] mean value: 0.013352751731872559 key: test_mcc value: [0.45524446 0.62325386 0.7098505 0.31980998 0.61237244 0.55629391 0.40201513 0.65743826 0.50251891 0.71443451] mean value: 0.5553231951012978 key: train_mcc value: [0.76399135 0.73767774 0.7547013 0.7861185 0.72109849 0.74765915 0.73210763 0.73737763 0.78097462 0.73397815] mean value: 0.7495684558929686 key: test_fscore value: [0.75 0.81818182 0.86363636 0.65 0.81818182 0.79069767 0.71428571 0.8372093 0.73684211 0.86363636] mean value: 0.7842671159929422 key: train_fscore value: [0.8847185 0.87301587 0.8806366 0.89487871 0.864 0.87567568 0.86933333 0.87165775 0.89247312 0.87139108] mean value: 0.8777780640064148 key: test_precision value: [0.64285714 0.75 0.82608696 0.68421053 0.75 0.73913043 0.68181818 0.7826087 0.77777778 0.79166667] mean value: 0.7426156382392081 key: train_precision value: [0.86387435 0.84183673 0.84693878 0.87368421 0.83937824 0.86170213 0.84455959 0.84895833 0.87368421 0.83417085] mean value: 0.8528787415904912 key: test_recall value: [0.9 0.9 0.9047619 0.61904762 0.9 0.85 0.75 0.9 0.7 0.95 ] mean value: 0.8373809523809523 key: train_recall value: [0.90659341 0.90659341 0.91712707 0.91712707 0.89010989 0.89010989 0.8956044 0.8956044 0.91208791 0.91208791] mean value: 0.9043045352437618 key: test_accuracy value: [0.70731707 0.80487805 0.85365854 0.65853659 0.8 0.775 0.7 0.825 0.75 0.85 ] mean value: 0.7724390243902439 key: train_accuracy value: [0.8815427 0.8677686 0.87603306 0.89256198 0.85989011 0.87362637 0.86538462 0.86813187 0.89010989 0.86538462] mean value: 0.8740433808615627 key: test_roc_auc value: [0.71190476 0.80714286 0.85238095 0.65952381 0.8 0.775 0.7 0.825 0.75 0.85 ] mean value: 0.7730952380952381 key: train_roc_auc value: [0.8814735 0.86766134 0.87614595 0.89262947 0.85989011 0.87362637 0.86538462 0.86813187 0.89010989 0.86538462] mean value: 0.8740437739056522 key: test_jcc value: [0.6 0.69230769 0.76 0.48148148 0.69230769 0.65384615 0.55555556 0.72 0.58333333 0.76 ] mean value: 0.6498831908831908 key: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( train_jcc value: [0.79326923 0.77464789 0.78672986 0.8097561 0.76056338 0.77884615 0.76886792 0.77251185 0.80582524 0.77209302] mean value: 0.7823110646445695 MCC on Blind test: 0.01 MCC on Training: 0.56 Running classifier: 10 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.03468871 0.04485059 0.03663397 0.03772545 0.06161284 0.08021903 0.03797793 0.04389906 0.06143379 0.07312775] mean value: 0.05121691226959228 key: score_time value: [0.02355194 0.01211047 0.01203752 0.01197267 0.02334642 0.01212716 0.01202559 0.01211548 0.03482151 0.01563358] mean value: 0.016974234580993654 key: test_mcc value: [0.70272837 0.59982886 0.7565654 0.51551459 0.75858261 0.60302269 0.71443451 0.85972695 0.60302269 0.80403025] mean value: 0.6917456920496428 key: train_mcc value: [0.9397456 0.93410669 0.91235166 0.9339425 0.90197069 0.91258396 0.92892204 0.90770392 0.91297033 0.90693572] mean value: 0.9191233097323437 key: test_fscore value: [0.85106383 0.80851064 0.88372093 0.75 0.88372093 0.80952381 0.86363636 0.93023256 0.78947368 0.9047619 ] mean value: 0.8474644648822363 key: train_fscore value: [0.9701897 0.9673913 0.95652174 0.96703297 0.9516129 0.95675676 0.96476965 0.95442359 0.95698925 0.95392954] mean value: 0.9599617399187805 key: test_precision value: [0.74074074 0.7037037 0.86363636 0.78947368 0.82608696 0.77272727 0.79166667 0.86956522 0.83333333 0.86363636] mean value: 0.8054570302568014 key: train_precision value: [0.95721925 0.95698925 0.94117647 0.96174863 0.93157895 0.94148936 0.95187166 0.93193717 0.93684211 0.94117647] mean value: 0.9452029318567565 key: test_recall value: [1. 0.95 0.9047619 0.71428571 0.95 0.85 0.95 1. 0.75 0.95 ] mean value: 0.9019047619047619 key: train_recall value: [0.98351648 0.97802198 0.97237569 0.97237569 0.97252747 0.97252747 0.97802198 0.97802198 0.97802198 0.96703297] mean value: 0.9752443688907777 key: test_accuracy value: [0.82926829 0.7804878 0.87804878 0.75609756 0.875 0.8 0.85 0.925 0.8 0.9 ] mean value: 0.8393902439024391 key: train_accuracy value: [0.96969697 0.96694215 0.95592287 0.96694215 0.95054945 0.95604396 0.96428571 0.9532967 0.95604396 0.9532967 ] mean value: 0.9593020615747887 key: test_roc_auc value: [0.83333333 0.78452381 0.87738095 0.75714286 0.875 0.8 0.85 0.925 0.8 0.9 ] mean value: 0.8402380952380952 key: train_roc_auc value: [0.96965879 0.96691154 0.95596807 0.96695708 0.95054945 0.95604396 0.96428571 0.9532967 0.95604396 0.9532967 ] mean value: 0.9593011960415275 key: test_jcc value: [0.74074074 0.67857143 0.79166667 0.6 0.79166667 0.68 0.76 0.86956522 0.65217391 0.82608696] mean value: 0.7390471589602025 key: train_jcc value: [0.94210526 0.93684211 0.91666667 0.93617021 0.90769231 0.91709845 0.93193717 0.91282051 0.91752577 0.9119171 ] mean value: 0.9230775558378692 MCC on Blind test: -0.1 MCC on Training: 0.69 Running classifier: 11 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.07359219 0.05869269 0.05934358 0.04325557 0.03610897 0.03702283 0.03706121 0.03799152 0.0369401 0.03582454] mean value: 0.04558331966400146 key: score_time value: [0.01215696 0.0119071 0.01200008 0.0119338 0.01187491 0.01191378 0.01188874 0.01192665 0.0119493 0.01182699] mean value: 0.011937832832336426 key: test_mcc value: [0.65952381 0.71121921 0.67700771 0.37171226 0.50251891 0.50251891 0.60302269 0.75858261 0.6289709 0.75858261] mean value: 0.6173659604195341 key: train_mcc value: [0.7796533 0.77419846 0.75217827 0.79615688 0.74176944 0.78572615 0.77473697 0.75275862 0.75824176 0.77473697] mean value: 0.7690156811215662 key: test_fscore value: [0.82926829 0.85714286 0.85106383 0.66666667 0.76190476 0.73684211 0.80952381 0.88372093 0.76470588 0.88372093] mean value: 0.8044560065789472 key: train_fscore value: [0.89071038 0.88828338 0.87671233 0.89807163 0.87123288 0.89256198 0.88767123 0.87603306 0.87912088 0.88705234] mean value: 0.884745008700176 key: test_precision value: [0.80952381 0.81818182 0.76923077 0.72222222 0.72727273 0.77777778 0.77272727 0.82608696 0.92857143 0.82608696] mean value: 0.7977681738551304 key: train_precision value: [0.88586957 0.88108108 0.86956522 0.8956044 0.86885246 0.89502762 0.8852459 0.87845304 0.87912088 0.88950276] mean value: 0.8828322924485155 key: test_recall value: [0.85 0.9 0.95238095 0.61904762 0.8 0.7 0.85 0.95 0.65 0.95 ] mean value: 0.8221428571428572 key: train_recall value: [0.8956044 0.8956044 0.8839779 0.90055249 0.87362637 0.89010989 0.89010989 0.87362637 0.87912088 0.88461538] mean value: 0.8866947969157914 key: test_accuracy value: [0.82926829 0.85365854 0.82926829 0.68292683 0.75 0.75 0.8 0.875 0.8 0.875 ] mean value: 0.8045121951219512 key: train_accuracy value: [0.88980716 0.88705234 0.87603306 0.89807163 0.87087912 0.89285714 0.88736264 0.87637363 0.87912088 0.88736264] mean value: 0.8844920231283867 key: test_roc_auc value: [0.8297619 0.8547619 0.82619048 0.68452381 0.75 0.75 0.8 0.875 0.8 0.875 ] mean value: 0.8045238095238094 key: train_roc_auc value: [0.88979115 0.88702872 0.87605488 0.89807844 0.87087912 0.89285714 0.88736264 0.87637363 0.87912088 0.88736264] mean value: 0.8844909234411997 key: test_jcc value: [0.70833333 0.75 0.74074074 0.5 0.61538462 0.58333333 0.68 0.79166667 0.61904762 0.79166667] mean value: 0.6780172975172977 key: train_jcc value: [0.80295567 0.79901961 0.7804878 0.815 0.77184466 0.80597015 0.79802956 0.77941176 0.78431373 0.7970297 ] mean value: 0.7934062637010345 MCC on Blind test: 0.01 MCC on Training: 0.62 Running classifier: 12 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(cv=3, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(cv=3, random_state=42))]) key: fit_time value: [0.49909115 0.50728321 0.56240559 0.47876453 0.50355554 0.49881363 0.65268636 0.48058724 0.48087811 0.49188828] mean value: 0.5155953645706177 key: score_time value: [0.01256371 0.01240611 0.01243806 0.01260018 0.01849437 0.01252985 0.01237464 0.01216722 0.01207328 0.01200795] mean value: 0.012965536117553711 key: test_mcc value: [0.65952381 0.75714286 0.61152662 0.56836003 0.6289709 0.60302269 0.80403025 0.75858261 0.61237244 0.75858261] mean value: 0.6762114807322386 key: train_mcc value: [0.82415655 0.82390091 0.9669419 1. 0.91214299 0.8299836 0.96703297 0.81362907 0.91208791 0.81318681] mean value: 0.8863062712926688 key: test_fscore value: [0.82926829 0.87804878 0.81818182 0.76923077 0.82608696 0.78947368 0.9047619 0.88372093 0.77777778 0.88372093] mean value: 0.8360271844320384 key: train_fscore value: [0.91061453 0.91111111 0.98342541 1. 0.9558011 0.91364903 0.98351648 0.90502793 0.95604396 0.90659341] mean value: 0.9425782959772171 key: test_precision value: [0.80952381 0.85714286 0.7826087 0.83333333 0.73076923 0.83333333 0.86363636 0.82608696 0.875 0.82608696] mean value: 0.823752153643458 key: train_precision value: [0.92613636 0.92134831 0.98342541 1. 0.96111111 0.92655367 0.98351648 0.92045455 0.95604396 0.90659341] mean value: 0.9485183267643633 key: test_recall value: [0.85 0.9 0.85714286 0.71428571 0.95 0.75 0.95 0.95 0.7 0.95 ] mean value: 0.8571428571428571 key: train_recall value: [0.8956044 0.9010989 0.98342541 1. 0.95054945 0.9010989 0.98351648 0.89010989 0.95604396 0.90659341] mean value: 0.9368040798980026 key: test_accuracy value: [0.82926829 0.87804878 0.80487805 0.7804878 0.8 0.8 0.9 0.875 0.8 0.875 ] mean value: 0.8342682926829269 key: train_accuracy value: [0.91184573 0.91184573 0.98347107 1. 0.95604396 0.91483516 0.98351648 0.90659341 0.95604396 0.90659341] mean value: 0.9430788908061636 key: test_roc_auc value: [0.8297619 0.87857143 0.80357143 0.78214286 0.8 0.8 0.9 0.875 0.8 0.875 ] mean value: 0.8344047619047619 key: train_roc_auc value: [0.9118906 0.91187542 0.98347095 1. 0.95604396 0.91483516 0.98351648 0.90659341 0.95604396 0.90659341] mean value: 0.9430863335559468 key: test_jcc value: [0.70833333 0.7826087 0.69230769 0.625 0.7037037 0.65217391 0.82608696 0.79166667 0.63636364 0.79166667] mean value: 0.7209911264259091 key: train_jcc value: [0.83589744 0.83673469 0.9673913 1. 0.91534392 0.84102564 0.96756757 0.82653061 0.91578947 0.82914573] mean value: 0.893542637263226 MCC on Blind test: 0.03 MCC on Training: 0.68 Running classifier: 13 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [1.42598867 1.47946668 1.54248571 1.42436719 1.57182598 1.76501465 1.79181695 1.43248487 1.58003378 1.54370189] mean value: 1.5557186365127564 key: score_time value: [0.01236129 0.01222134 0.01211858 0.01215339 0.01367307 0.01720643 0.0121243 0.01252913 0.01226211 0.03191876] mean value: 0.014856839179992675 key: test_mcc value: [0.75714286 0.76500781 0.8047619 0.36666667 0.65743826 0.65081403 0.8510645 0.85972695 0.61237244 0.85972695] mean value: 0.7184722360288601 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.87804878 0.88372093 0.9047619 0.68292683 0.8372093 0.82051282 0.92682927 0.93023256 0.77777778 0.93023256] mean value: 0.8572252729938492 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.85714286 0.82608696 0.9047619 0.7 0.7826087 0.84210526 0.9047619 0.86956522 0.875 0.86956522] mean value: 0.8431598016781082 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.9 0.95 0.9047619 0.66666667 0.9 0.8 0.95 1. 0.7 1. ] mean value: 0.8771428571428572 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.87804878 0.87804878 0.90243902 0.68292683 0.825 0.825 0.925 0.925 0.8 0.925 ] mean value: 0.8566463414634147 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.87857143 0.8797619 0.90238095 0.68333333 0.825 0.825 0.925 0.925 0.8 0.925 ] mean value: 0.8569047619047618 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.7826087 0.79166667 0.82608696 0.51851852 0.72 0.69565217 0.86363636 0.86956522 0.63636364 0.86956522] mean value: 0.7573663446054751 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.01 MCC on Training: 0.72 Running classifier: 14 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.0130837 0.01340961 0.0103581 0.01102471 0.01055717 0.01044798 0.01037788 0.01041174 0.01064539 0.01050949] mean value: 0.0110825777053833 key: score_time value: [0.01203465 0.01042366 0.00959921 0.0098536 0.00991201 0.0093925 0.00997233 0.00986242 0.00975823 0.00961709] mean value: 0.010042572021484375 key: test_mcc value: [0.48063079 0.6133669 0.42916625 0.4373371 0.30618622 0.55629391 0.464758 0.25286087 0.56803756 0.52414242] mean value: 0.46327800137240016 key: train_mcc value: [0.52472332 0.53869386 0.53023241 0.52075048 0.48099517 0.56697057 0.52747253 0.55495343 0.5841484 0.44565652] mean value: 0.5274596688993471 key: test_fscore value: [0.75555556 0.80952381 0.75 0.66666667 0.68181818 0.75675676 0.75555556 0.65116279 0.74285714 0.7826087 ] mean value: 0.7352505155083517 key: train_fscore value: [0.7751938 0.77777778 0.77604167 0.76164384 0.75879397 0.78933333 0.76373626 0.7768595 0.7989418 0.74111675] mean value: 0.7719438699772405 key: test_precision value: [0.68 0.77272727 0.66666667 0.8 0.625 0.82352941 0.68 0.60869565 0.86666667 0.69230769] mean value: 0.7215593362306918 key: train_precision value: [0.73170732 0.75 0.73399015 0.75543478 0.69907407 0.76683938 0.76373626 0.77900552 0.77040816 0.68867925] mean value: 0.7438874896924 key: test_recall value: [0.85 0.85 0.85714286 0.57142857 0.75 0.7 0.85 0.7 0.65 0.9 ] mean value: 0.7678571428571429 key: train_recall value: [0.82417582 0.80769231 0.82320442 0.7679558 0.82967033 0.81318681 0.76373626 0.77472527 0.82967033 0.8021978 ] mean value: 0.803621516604942 key: test_accuracy value: [0.73170732 0.80487805 0.70731707 0.70731707 0.65 0.775 0.725 0.625 0.775 0.75 ] mean value: 0.7251219512195123 key: train_accuracy value: [0.76033058 0.76859504 0.7630854 0.76033058 0.73626374 0.78296703 0.76373626 0.77747253 0.79120879 0.71978022] mean value: 0.7623770169224715 key: test_roc_auc value: [0.73452381 0.80595238 0.70357143 0.71071429 0.65 0.775 0.725 0.625 0.775 0.75 ] mean value: 0.7254761904761905 key: train_roc_auc value: [0.76015421 0.76848704 0.76325056 0.76035153 0.73626374 0.78296703 0.76373626 0.77747253 0.79120879 0.71978022] mean value: 0.7623671908202294 key: test_jcc value: [0.60714286 0.68 0.6 0.5 0.51724138 0.60869565 0.60714286 0.48275862 0.59090909 0.64285714] mean value: 0.5836747600225861 key: train_jcc value: [0.63291139 0.63636364 0.63404255 0.61504425 0.61133603 0.65198238 0.61777778 0.63513514 0.66519824 0.58870968] mean value: 0.6288501069208821 MCC on Blind test: -0.05 MCC on Training: 0.46 Running classifier: 15 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.0107348 0.01142359 0.01030898 0.00978088 0.01143456 0.01007509 0.00994158 0.01146984 0.01122451 0.01128674] mean value: 0.01076805591583252 key: score_time value: [0.01027894 0.01001525 0.0095284 0.00986648 0.01041389 0.00985551 0.01041675 0.01037669 0.0103476 0.01433301] mean value: 0.010543251037597656 key: test_mcc value: [0.26904762 0.52420964 0.70714286 0.47003614 0.464758 0.35043832 0.56803756 0.6289709 0.75093926 0.67131711] mean value: 0.5404897423638162 key: train_mcc value: [0.64392154 0.61797068 0.59354183 0.64818006 0.6016363 0.63189177 0.59955605 0.59955605 0.62589712 0.60886023] mean value: 0.6171011630137258 key: test_fscore value: [0.63414634 0.77272727 0.85714286 0.71794872 0.75555556 0.68292683 0.8 0.82608696 0.87804878 0.84444444] mean value: 0.77690277555601 key: train_fscore value: [0.83163265 0.81770833 0.80719794 0.83204134 0.81218274 0.82474227 0.81025641 0.81025641 0.82170543 0.8134715 ] mean value: 0.8181195032126609 key: test_precision value: [0.61904762 0.70833333 0.85714286 0.77777778 0.68 0.66666667 0.72 0.73076923 0.85714286 0.76 ] mean value: 0.7376880341880342 key: train_precision value: [0.77619048 0.77722772 0.75480769 0.7815534 0.75471698 0.77669903 0.75961538 0.75961538 0.77560976 0.76960784] mean value: 0.7685643668052572 key: test_recall value: [0.65 0.85 0.85714286 0.66666667 0.85 0.7 0.9 0.95 0.9 0.95 ] mean value: 0.8273809523809523 key: train_recall value: [0.8956044 0.86263736 0.86740331 0.88950276 0.87912088 0.87912088 0.86813187 0.86813187 0.87362637 0.86263736] mean value: 0.8745917066359056 key: test_accuracy value: [0.63414634 0.75609756 0.85365854 0.73170732 0.725 0.675 0.775 0.8 0.875 0.825 ] mean value: 0.7650609756097562 key: train_accuracy value: [0.81818182 0.80716253 0.79338843 0.82093664 0.7967033 0.81318681 0.7967033 0.7967033 0.81043956 0.8021978 ] mean value: 0.8055603487421671 key: test_roc_auc value: [0.63452381 0.75833333 0.85357143 0.73333333 0.725 0.675 0.775 0.8 0.875 0.825 ] mean value: 0.7654761904761904 key: train_roc_auc value: [0.81796794 0.80700929 0.79359177 0.82112501 0.7967033 0.81318681 0.7967033 0.7967033 0.81043956 0.8021978 ] mean value: 0.8055628073583876 key: test_jcc value: [0.46428571 0.62962963 0.75 0.56 0.60714286 0.51851852 0.66666667 0.7037037 0.7826087 0.73076923] mean value: 0.6413325016368494 key: train_jcc value: [0.71179039 0.69162996 0.67672414 0.71238938 0.68376068 0.70175439 0.68103448 0.68103448 0.69736842 0.68558952] mean value: 0.6923075843368369 MCC on Blind test: 0.0 MCC on Training: 0.54 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.01447868 0.02209544 0.02071381 0.02348065 0.0190165 0.01902223 0.02273822 0.01899624 0.02098012 0.01887178] mean value: 0.02003936767578125 key: score_time value: [0.0085907 0.01191068 0.01232362 0.01235223 0.01221275 0.01229501 0.01206303 0.01190543 0.0120225 0.01198101] mean value: 0.011765694618225098 key: test_mcc value: [0.62325386 0.62325386 0.7098505 0.52052166 0.33333333 0.52414242 0.53881591 0.70352647 0.58713656 0.67131711] mean value: 0.5835151700581372 key: train_mcc value: [0.76126269 0.72476389 0.81387896 0.66551466 0.34141531 0.5434829 0.57748674 0.75625612 0.77104514 0.53074489] mean value: 0.6485851301562942 /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") key: test_fscore value: [0.81818182 0.81818182 0.86363636 0.66666667 0.71428571 0.7826087 0.78431373 0.84210526 0.72727273 0.8 ] mean value: 0.7817252792525373 key: train_fscore value: [0.88481675 0.86848635 0.90340909 0.76027397 0.71653543 0.78617711 0.80088496 0.86309524 0.87315634 0.61068702] mean value: 0.8067522267629357 key: test_precision value: [0.75 0.75 0.82608696 0.91666667 0.55555556 0.69230769 0.64516129 0.88888889 0.92307692 0.93333333] mean value: 0.788107730667338 key: train_precision value: [0.845 0.7918552 0.92982456 1. 0.55828221 0.64768683 0.67037037 0.94155844 0.94267516 1. ] mean value: 0.8327252777517071 key: test_recall value: [0.9 0.9 0.9047619 0.52380952 1. 0.9 1. 0.8 0.6 0.7 ] mean value: 0.8228571428571427 key: train_recall value: [0.92857143 0.96153846 0.87845304 0.61325967 1. 1. 0.99450549 0.7967033 0.81318681 0.43956044] mean value: 0.8425778641248254 key: test_accuracy value: [0.80487805 0.80487805 0.85365854 0.73170732 0.6 0.75 0.725 0.85 0.775 0.825 ] mean value: 0.7720121951219513 key: train_accuracy value: [0.87878788 0.85399449 0.90633609 0.80716253 0.6043956 0.72802198 0.75274725 0.87362637 0.88186813 0.71978022] mean value: 0.8006720552175096 key: test_roc_auc value: [0.80714286 0.80714286 0.85238095 0.73690476 0.6 0.75 0.725 0.85 0.775 0.825 ] mean value: 0.7728571428571429 key: train_roc_auc value: [0.87865036 0.85369741 0.90625949 0.80662983 0.6043956 0.72802198 0.75274725 0.87362637 0.88186813 0.71978022] mean value: 0.8005676643798191 key: test_jcc value: [0.69230769 0.69230769 0.76 0.5 0.55555556 0.64285714 0.64516129 0.72727273 0.57142857 0.66666667] mean value: 0.645355733871863 key: train_jcc value: [0.79342723 0.76754386 0.8238342 0.61325967 0.55828221 0.64768683 0.66789668 0.7591623 0.77486911 0.43956044] mean value: 0.6845522528564516 MCC on Blind test: 0.06 MCC on Training: 0.58 Running classifier: 17 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.04051733 0.02680516 0.02825236 0.02984047 0.02679443 0.02846909 0.02677727 0.02818751 0.02839589 0.02732754] mean value: 0.02913670539855957 key: score_time value: [0.01322532 0.0126195 0.01256013 0.01293111 0.03075242 0.01258612 0.01258659 0.01254272 0.01263738 0.01248455] mean value: 0.01449258327484131 key: test_mcc value: [0.95227002 0.95238095 1. 0.90692382 1. 0.95118973 1. 1. 0.95118973 1. ] mean value: 0.9713954253972036 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.97435897 0.97560976 1. 0.95 1. 0.97560976 1. 1. 0.97560976 1. ] mean value: 0.9851188242651657 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.95238095 1. 1. 1. 0.95238095 1. 1. 0.95238095 1. ] mean value: 0.9857142857142858 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.95 1. 1. 0.9047619 1. 1. 1. 1. 1. 1. ] mean value: 0.9854761904761904 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.97560976 0.97560976 1. 0.95121951 1. 0.975 1. 1. 0.975 1. ] mean value: 0.9852439024390243 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.975 0.97619048 1. 0.95238095 1. 0.975 1. 1. 0.975 1. ] mean value: 0.9853571428571428 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.95 0.95238095 1. 0.9047619 1. 0.95238095 1. 1. 0.95238095 1. ] mean value: 0.9711904761904762 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.26 MCC on Training: 0.97 Running classifier: 18 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42))]) key: fit_time value: [0.65816784 0.74204588 0.6814332 0.65268636 0.64846826 0.65622759 0.65671587 0.67372274 0.64662457 0.67176437] mean value: 0.6687856674194336 key: score_time value: [0.1712575 0.19699121 0.18284464 0.17982531 0.16482759 0.18974352 0.15461755 0.1534102 0.17089319 0.18865108] mean value: 0.17530617713928223 key: test_mcc value: [0.7565654 0.80907152 0.90238095 0.71121921 0.8510645 0.70352647 0.75093926 0.90453403 0.71443451 0.95118973] mean value: 0.8054925580678145 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.87179487 0.9047619 0.95238095 0.85 0.92682927 0.84210526 0.87179487 0.95238095 0.83333333 0.97435897] mean value: 0.8979740392256439 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.89473684 0.86363636 0.95238095 0.89473684 0.9047619 0.88888889 0.89473684 0.90909091 0.9375 1. ] mean value: 0.9140469545074807 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.85 0.95 0.95238095 0.80952381 0.95 0.8 0.85 1. 0.75 0.95 ] mean value: 0.8861904761904761 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.87804878 0.90243902 0.95121951 0.85365854 0.925 0.85 0.875 0.95 0.85 0.975 ] mean value: 0.9010365853658536 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.87738095 0.90357143 0.95119048 0.8547619 0.925 0.85 0.875 0.95 0.85 0.975 ] mean value: 0.9011904761904761 key: train_roc_auc value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.77272727 0.82608696 0.90909091 0.73913043 0.86363636 0.72727273 0.77272727 0.90909091 0.71428571 0.95 ] mean value: 0.8184048560135515 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.26 MCC on Training: 0.81 Running classifier: 19 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea...age_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42))]) key: fit_time value: [0.96849108 1.03998899 0.96207285 1.04708028 0.98185086 0.986485 1.00486803 1.05906034 1.01989675 0.95828342] mean value: 1.0028077602386474 key: score_time value: [0.21675611 0.23478031 0.22339606 0.24405098 0.16501617 0.22578239 0.18078518 0.20910406 0.23804665 0.18030143] mean value: 0.21180193424224852 key: test_mcc value: [0.66432098 0.8547619 0.90238095 0.71121921 0.8 0.71443451 0.65081403 0.95118973 0.41931393 1. ] mean value: 0.7668435245660057 key: train_mcc value: [0.96156532 0.95615741 0.97258321 0.97281876 0.96709136 0.97265962 0.9505638 0.95610169 0.95627494 0.97265962] mean value: 0.9638475729641292 key: test_fscore value: [0.81081081 0.92682927 0.95238095 0.85 0.9 0.83333333 0.82051282 0.97560976 0.64705882 1. ] mean value: 0.8716535764957571 key: train_fscore value: [0.98060942 0.97777778 0.98607242 0.9859944 0.98342541 0.98614958 0.97520661 0.97790055 0.97777778 0.98614958] mean value: 0.9817063542391683 key: test_precision value: [0.88235294 0.9047619 0.95238095 0.89473684 0.9 0.9375 0.84210526 0.95238095 0.78571429 1. ] mean value: 0.9051933141677724 key: train_precision value: [0.98882682 0.98876404 0.99438202 1. 0.98888889 0.99441341 0.97790055 0.98333333 0.98876404 0.99441341] mean value: 0.9899686518352876 key: test_recall value: [0.75 0.95 0.95238095 0.80952381 0.9 0.75 0.8 1. 0.55 1. ] mean value: 0.8461904761904762 key: train_recall value: [0.97252747 0.96703297 0.97790055 0.97237569 0.97802198 0.97802198 0.97252747 0.97252747 0.96703297 0.97802198] mean value: 0.9735990528808209 key: test_accuracy value: [0.82926829 0.92682927 0.95121951 0.85365854 0.9 0.85 0.825 0.975 0.7 1. ] mean value: 0.8810975609756098 key: train_accuracy value: [0.98071625 0.97796143 0.9862259 0.9862259 0.98351648 0.98626374 0.97527473 0.97802198 0.97802198 0.98626374] mean value: 0.9818492113946661 key: test_roc_auc value: [0.82738095 0.92738095 0.95119048 0.8547619 0.9 0.85 0.825 0.975 0.7 1. ] mean value: 0.8810714285714287 key: train_roc_auc value: [0.98073887 0.97799162 0.98620302 0.98618785 0.98351648 0.98626374 0.97527473 0.97802198 0.97802198 0.98626374] mean value: 0.9818484002185659 key: test_jcc value: [0.68181818 0.86363636 0.90909091 0.73913043 0.81818182 0.71428571 0.69565217 0.95238095 0.47826087 1. ] mean value: 0.785243741765481 key: train_jcc value: [0.96195652 0.95652174 0.97252747 0.97237569 0.9673913 0.9726776 0.9516129 0.95675676 0.95652174 0.9726776 ] mean value: 0.9641019318722428 MCC on Blind test: 0.26 MCC on Training: 0.77 Running classifier: 20 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.01972675 0.04470277 0.03630424 0.01705241 0.05261636 0.02707458 0.01515865 0.01476502 0.01500773 0.03440166] mean value: 0.02768101692199707 key: score_time value: [0.02300453 0.03081036 0.03408933 0.0449326 0.02109718 0.01239347 0.01191497 0.01205444 0.01193118 0.02123046] mean value: 0.022345852851867676 key: test_mcc value: [0.65952381 0.62325386 0.7565654 0.36666667 0.71443451 0.6 0.7 0.80403025 0.51031036 0.80403025] mean value: 0.6538815115857697 key: train_mcc value: [0.85680459 0.8567959 0.8512492 0.87329245 0.84661403 0.85760902 0.86265038 0.86818429 0.85719462 0.86813187] mean value: 0.859852635550862 key: test_fscore value: [0.82926829 0.81818182 0.88372093 0.68292683 0.86363636 0.8 0.85 0.9047619 0.72222222 0.9047619 ] mean value: 0.825948026574799 key: train_fscore value: [0.9281768 0.92896175 0.92520776 0.93663912 0.92178771 0.9273743 0.93150685 0.93370166 0.92896175 0.93406593] mean value: 0.9296383619550609 key: test_precision value: [0.80952381 0.75 0.86363636 0.7 0.79166667 0.8 0.85 0.86363636 0.8125 0.86363636] mean value: 0.8104599567099567 key: train_precision value: [0.93333333 0.92391304 0.92777778 0.93406593 0.9375 0.94318182 0.92896175 0.93888889 0.92391304 0.93406593] mean value: 0.9325601521904089 key: test_recall value: [0.85 0.9 0.9047619 0.66666667 0.95 0.8 0.85 0.95 0.65 0.95 ] mean value: 0.8471428571428572 key: train_recall value: [0.92307692 0.93406593 0.92265193 0.93922652 0.90659341 0.91208791 0.93406593 0.92857143 0.93406593 0.93406593] mean value: 0.9268471859632081 key: test_accuracy value: [0.82926829 0.80487805 0.87804878 0.68292683 0.85 0.8 0.85 0.9 0.75 0.9 ] mean value: 0.8245121951219513 key: train_accuracy value: [0.92837466 0.92837466 0.92561983 0.93663912 0.92307692 0.92857143 0.93131868 0.93406593 0.92857143 0.93406593] mean value: 0.929867859413314 key: test_roc_auc value: [0.8297619 0.80714286 0.87738095 0.68333333 0.85 0.8 0.85 0.9 0.75 0.9 ] mean value: 0.8247619047619047 key: train_roc_auc value: [0.92838929 0.92835893 0.92561168 0.93664623 0.92307692 0.92857143 0.93131868 0.93406593 0.92857143 0.93406593] mean value: 0.9298676461659887 key: test_jcc value: [0.70833333 0.69230769 0.79166667 0.51851852 0.76 0.66666667 0.73913043 0.82608696 0.56521739 0.82608696] mean value: 0.7094014616623312 key: train_jcc value: [0.86597938 0.86734694 0.86082474 0.88082902 0.85492228 0.86458333 0.87179487 0.87564767 0.86734694 0.87628866] mean value: 0.8685563829914951 MCC on Blind test: -0.01 MCC on Training: 0.65 Running classifier: 21 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=3))]) key: fit_time value: [0.16056299 0.15872669 0.13242197 0.1305306 0.10846758 0.09997797 0.09061766 0.12015748 0.13194299 0.11342096] mean value: 0.12468268871307372 key: score_time value: [0.01789427 0.03830838 0.02432013 0.02076268 0.02012229 0.02012801 0.02322769 0.01684356 0.02049494 0.02220249] mean value: 0.02243044376373291 key: test_mcc value: [0.72229808 0.62325386 0.7565654 0.56836003 0.67131711 0.65743826 0.7 0.85972695 0.60302269 0.8510645 ] mean value: 0.701304688917615 key: train_mcc value: [0.92849771 0.8567959 0.90646347 0.9339425 0.90131661 0.92329994 0.86265038 0.89626756 0.93429161 0.90131661] mean value: 0.904484229374171 key: test_fscore value: [0.86363636 0.81818182 0.88372093 0.76923077 0.84444444 0.8372093 0.85 0.93023256 0.78947368 0.92682927] mean value: 0.8512959138694278 key: train_fscore value: [0.96457766 0.92896175 0.95342466 0.96703297 0.95108696 0.96195652 0.93150685 0.94878706 0.9673913 0.95108696] mean value: 0.9525812680316955 key: test_precision value: [0.79166667 0.75 0.86363636 0.83333333 0.76 0.7826087 0.85 0.86956522 0.83333333 0.9047619 ] mean value: 0.8238905514775079 key: train_precision value: [0.95675676 0.92391304 0.94565217 0.96174863 0.94086022 0.9516129 0.92896175 0.93121693 0.95698925 0.94086022] mean value: 0.9438571868523816 key: test_recall value: [0.95 0.9 0.9047619 0.71428571 0.95 0.9 0.85 1. 0.75 0.95 ] mean value: 0.8869047619047619 key: train_recall value: [0.97252747 0.93406593 0.96132597 0.97237569 0.96153846 0.97252747 0.93406593 0.96703297 0.97802198 0.96153846] mean value: 0.9615020338777244 key: test_accuracy value: [0.85365854 0.80487805 0.87804878 0.7804878 0.825 0.825 0.85 0.925 0.8 0.925 ] mean value: 0.8467073170731707 key: train_accuracy value: [0.96418733 0.92837466 0.95316804 0.96694215 0.95054945 0.96153846 0.93131868 0.9478022 0.96703297 0.95054945] mean value: 0.9521463385099749 key: test_roc_auc value: [0.85595238 0.80714286 0.87738095 0.78214286 0.825 0.825 0.85 0.925 0.8 0.925 ] mean value: 0.8472619047619047 key: train_roc_auc value: [0.96416429 0.92835893 0.95319046 0.96695708 0.95054945 0.96153846 0.93131868 0.9478022 0.96703297 0.95054945] mean value: 0.9521461963450915 key: test_jcc value: [0.76 0.69230769 0.79166667 0.625 0.73076923 0.72 0.73913043 0.86956522 0.65217391 0.86363636] mean value: 0.7444249518597343 key: train_jcc value: [0.93157895 0.86734694 0.91099476 0.93617021 0.90673575 0.92670157 0.87179487 0.9025641 0.93684211 0.90673575] mean value: 0.9097465016201228 MCC on Blind test: -0.05 MCC on Training: 0.7 Running classifier: 22 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.0383873 0.01561451 0.01536465 0.01513815 0.01536155 0.01525378 0.0151782 0.01522732 0.01518083 0.01538253] mean value: 0.0176088809967041 key: score_time value: [0.01094198 0.01024699 0.01014471 0.01017284 0.01055241 0.01026893 0.01024365 0.0103631 0.01026583 0.01047206] mean value: 0.010367250442504883 key: test_mcc value: [0.70714286 0.71121921 0.85441771 0.52420964 0.65743826 0.51031036 0.7 0.65743826 0.61237244 0.71443451] mean value: 0.6648983243463289 key: train_mcc value: [0.76859329 0.77419846 0.77437977 0.79067367 0.77090828 0.79163909 0.78026689 0.78064411 0.76384004 0.75842495] mean value: 0.7753568553908416 key: test_fscore value: [0.85 0.85714286 0.93023256 0.73684211 0.81081081 0.72222222 0.85 0.8372093 0.77777778 0.86363636] mean value: 0.8235873997318306 key: train_fscore value: [0.88461538 0.88828338 0.88515406 0.89444444 0.88068182 0.89385475 0.88950276 0.88826816 0.88088643 0.87777778] mean value: 0.8863468959442338 key: test_precision value: [0.85 0.81818182 0.90909091 0.82352941 0.88235294 0.8125 0.85 0.7826087 0.875 0.79166667] mean value: 0.8394930442532743 key: train_precision value: [0.88461538 0.88108108 0.89772727 0.89944134 0.91176471 0.90909091 0.89444444 0.90340909 0.88826816 0.88764045] mean value: 0.895748283539544 key: test_recall value: [0.85 0.9 0.95238095 0.66666667 0.75 0.65 0.85 0.9 0.7 0.95 ] mean value: 0.8169047619047619 key: train_recall value: [0.88461538 0.8956044 0.87292818 0.88950276 0.85164835 0.87912088 0.88461538 0.87362637 0.87362637 0.86813187] mean value: 0.8773419950215532 key: test_accuracy value: [0.85365854 0.85365854 0.92682927 0.75609756 0.825 0.75 0.85 0.825 0.8 0.85 ] mean value: 0.8290243902439025 key: train_accuracy value: [0.88429752 0.88705234 0.88705234 0.8953168 0.88461538 0.8956044 0.89010989 0.89010989 0.88186813 0.87912088] mean value: 0.8875147579693035 key: test_roc_auc value: [0.85357143 0.8547619 0.92619048 0.75833333 0.825 0.75 0.85 0.825 0.8 0.85 ] mean value: 0.8292857142857143 key: train_roc_auc value: [0.88429664 0.88702872 0.88701354 0.89530083 0.88461538 0.8956044 0.89010989 0.89010989 0.88186813 0.87912088] mean value: 0.8875068301863882 key: test_jcc value: [0.73913043 0.75 0.86956522 0.58333333 0.68181818 0.56521739 0.73913043 0.72 0.63636364 0.76 ] mean value: 0.7044558629776021 key: train_jcc value: [0.79310345 0.79901961 0.79396985 0.80904523 0.78680203 0.80808081 0.80099502 0.79899497 0.78712871 0.78217822] mean value: 0.7959317900476608 MCC on Blind test: -0.06 MCC on Training: 0.66 Running classifier: 23 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=12, random_state=42) Running model pipeline: /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:463: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_CV['source_data'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:490: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_BT['source_data'] = 'BT' Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.01377797 0.01894164 0.01699615 0.02070761 0.01978064 0.01644778 0.01876712 0.0201962 0.01972222 0.01929569] mean value: 0.01846330165863037 key: score_time value: [0.00864649 0.01164794 0.01216316 0.01232243 0.01235342 0.01222324 0.01232123 0.01233578 0.01241183 0.01236963] mean value: 0.011879515647888184 key: test_mcc value: [0.7197263 0.66668392 0.75714286 0.41766229 0.45514956 0.46188022 0.75093926 0.8 0.50251891 0.75858261] mean value: 0.6290285922307224 key: train_mcc value: [0.7816334 0.82930533 0.7988293 0.87033365 0.77989891 0.32616404 0.82542272 0.80020174 0.84128182 0.83536661] mean value: 0.7688437530787804 key: test_fscore value: [0.83333333 0.8372093 0.87804878 0.7 0.7027027 0.76 0.87179487 0.9 0.73684211 0.88372093] mean value: 0.8103652026140009 key: train_fscore value: [0.87905605 0.91553134 0.89337176 0.93103448 0.8742515 0.71232877 0.90960452 0.89337176 0.92183288 0.91847826] mean value: 0.8848861309826155 key: test_precision value: [0.9375 0.7826087 0.9 0.73684211 0.76470588 0.63333333 0.89473684 0.9 0.77777778 0.82608696] mean value: 0.8153591593006386 key: train_precision value: [0.94904459 0.90810811 0.93373494 0.97005988 0.96052632 0.55319149 0.93604651 0.93939394 0.9047619 0.90860215] mean value: 0.8963469825566488 key: test_recall value: [0.75 0.9 0.85714286 0.66666667 0.65 0.95 0.85 0.9 0.7 0.95 ] mean value: 0.8173809523809524 key: train_recall value: [0.81868132 0.92307692 0.85635359 0.89502762 0.8021978 1. 0.88461538 0.85164835 0.93956044 0.92857143] mean value: 0.8899732863821261 key: test_accuracy value: [0.85365854 0.82926829 0.87804878 0.70731707 0.725 0.7 0.875 0.9 0.75 0.875 ] mean value: 0.8093292682926829 key: train_accuracy value: [0.88705234 0.91460055 0.89807163 0.9338843 0.88461538 0.59615385 0.91208791 0.89835165 0.92032967 0.91758242] mean value: 0.8762729694547875 key: test_roc_auc value: [0.85119048 0.83095238 0.87857143 0.70833333 0.725 0.7 0.875 0.9 0.75 0.875 ] mean value: 0.8094047619047618 key: train_roc_auc value: [0.88724121 0.91457714 0.89795702 0.93377755 0.88461538 0.59615385 0.91208791 0.89835165 0.92032967 0.91758242] mean value: 0.87626737902981 key: test_jcc value: [0.71428571 0.72 0.7826087 0.53846154 0.54166667 0.61290323 0.77272727 0.81818182 0.58333333 0.79166667] mean value: 0.6875834931781635 key: train_jcc value: [0.78421053 0.84422111 0.80729167 0.87096774 0.77659574 0.55319149 0.83419689 0.80729167 0.855 0.84924623] mean value: 0.7982213063502287 MCC on Blind test: 0.11 MCC on Training: 0.63 Running classifier: 24 Model_name: XGBoost Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.12685537 0.09283853 0.25876904 0.08956027 0.09169602 0.11412668 0.09264803 0.09295607 0.09026504 0.10049772] mean value: 0.11502127647399903 key: score_time value: [0.01085401 0.01120663 0.01192641 0.01090932 0.01091647 0.01219559 0.01104736 0.01111531 0.01183772 0.01196384] mean value: 0.011397266387939453 key: test_mcc value: [0.60952381 0.78072006 0.8047619 0.70714286 0.75858261 0.71443451 0.75093926 0.95118973 0.77459667 0.9 ] mean value: 0.7751891406170882 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.8 0.88888889 0.9047619 0.85714286 0.88372093 0.83333333 0.87804878 0.97560976 0.85714286 0.95 ] mean value: 0.8828649308087766 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.8 0.8 0.9047619 0.85714286 0.82608696 0.9375 0.85714286 0.95238095 1. 0.95 ] mean value: 0.8885015527950311 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.8 1. 0.9047619 0.85714286 0.95 0.75 0.9 1. 0.75 0.95 ] mean value: 0.8861904761904761 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.80487805 0.87804878 0.90243902 0.85365854 0.875 0.85 0.875 0.975 0.875 0.95 ] mean value: 0.8839024390243904 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.8047619 0.88095238 0.90238095 0.85357143 0.875 0.85 0.875 0.975 0.875 0.95 ] mean value: 0.8841666666666667 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.66666667 0.8 0.82608696 0.75 0.79166667 0.71428571 0.7826087 0.95238095 0.75 0.9047619 ] mean value: 0.7938457556935818 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.34 MCC on Training: 0.78 Extracting tts_split_name: 80_20 Total cols in each df: CV df: 8 metaDF: 17 Adding column: Model_name Total cols in bts df: BT_df: 8 First proceeding to rowbind CV and BT dfs: Final output should have: 25 columns Combinig 2 using pd.concat by row ~ rowbind Checking Dims of df to combine: Dim of CV: (24, 8) Dim of BT: (24, 8) 8 Number of Common columns: 8 These are: [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... 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Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... 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Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... 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Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished ['source_data', 'Precision', 'JCC', 'MCC', 'Recall', 'Accuracy', 'F1', 'ROC_AUC'] Concatenating dfs with different resampling methods [WF]: Split type: 80_20 No. of dfs combining: 2 PASS: 2 dfs successfully combined nrows in combined_df_wf: 48 ncols in combined_df_wf: 8 PASS: proceeding to merge metadata with CV and BT dfs Adding column: Model_name ========================================================= SUCCESS: Ran multiple classifiers ======================================================= ============================================================== Running several classification models (n): 24 List of models: ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('Gaussian NB', GaussianNB()) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('K-Nearest Neighbors', KNeighborsClassifier()) ('LDA', LinearDiscriminantAnalysis()) ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(cv=3, random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Multinomial', MultinomialNB()) ('Naive Bayes', BernoulliNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=12, random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Random Forest', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42)) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=3)) ('SVC', SVC(random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=12, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ================================================================ Running classifier: 1 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.13997865 0.13951349 0.14061046 0.14242029 0.14572716 0.14103913 0.14135098 0.14134026 0.14221644 0.13848066] mean value: 0.1412677526473999 key: score_time value: [0.01470184 0.01495409 0.01617217 0.01515555 0.01504683 0.01614404 0.01478577 0.01479721 0.0157454 0.01470852] mean value: 0.015221142768859863 key: test_mcc value: [0.75714286 0.8213423 0.85441771 0.81975606 0.75858261 0.60302269 0.73379939 0.95118973 0.8510645 0.90453403] mean value: 0.8054851869075176 key: train_mcc value: [0.97246592 1. 0.97802131 0.98348613 0.99452051 0.96726661 0.98353133 0.96709136 0.97802198 0.96726661] mean value: 0.979167175094825 key: test_fscore value: [0.87804878 0.90909091 0.93023256 0.91304348 0.88372093 0.80952381 0.86956522 0.97560976 0.92682927 0.95238095] mean value: 0.9048045659897987 key: train_fscore value: [0.98630137 1. 0.98901099 0.99173554 0.99726027 0.98369565 0.99178082 0.98360656 0.98901099 0.98369565] mean value: 0.9896097842690359 key: test_precision value: [0.85714286 0.83333333 0.90909091 0.84 0.82608696 0.77272727 0.76923077 0.95238095 0.9047619 0.90909091] mean value: 0.8573845864280647 key: train_precision value: [0.98360656 1. 0.98360656 0.98901099 0.99453552 0.97311828 0.98907104 0.97826087 0.98901099 0.97311828] mean value: 0.9853339078858128 key: test_recall value: [0.9 1. 0.95238095 1. 0.95 0.85 1. 1. 0.95 1. ] mean value: 0.9602380952380951 key: train_recall value: [0.98901099 1. 0.99447514 0.99447514 1. 0.99450549 0.99450549 0.98901099 0.98901099 0.99450549] mean value: 0.9939499726792546 key: test_accuracy value: [0.87804878 0.90243902 0.92682927 0.90243902 0.875 0.8 0.85 0.975 0.925 0.95 ] mean value: 0.8984756097560975 key: train_accuracy value: [0.9862259 1. 0.98898072 0.99173554 0.99725275 0.98351648 0.99175824 0.98351648 0.98901099 0.98351648] mean value: 0.989551357733176 key: test_roc_auc value: [0.87857143 0.9047619 0.92619048 0.9 0.875 0.8 0.85 0.975 0.925 0.95 ] mean value: 0.8984523809523809 key: train_roc_auc value: [0.9862182 1. 0.98899581 0.99174306 0.99725275 0.98351648 0.99175824 0.98351648 0.98901099 0.98351648] mean value: 0.9895528504644527 key: test_jcc value: [0.7826087 0.83333333 0.86956522 0.84 0.79166667 0.68 0.76923077 0.95238095 0.86363636 0.90909091] mean value: 0.8291512907382472 key: train_jcc value: [0.97297297 1. 0.97826087 0.98360656 0.99453552 0.96791444 0.98369565 0.96774194 0.97826087 0.96791444] mean value: 0.9794903253269271 MCC on Blind test: 0.21 MCC on Training: 0.81 Running classifier: 2 Model_name: Bagging Classifier Model func: BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3))]) key: fit_time value: [0.20788407 0.21920967 0.21815848 0.22176719 0.21049261 0.21670055 0.20770526 0.23438716 0.20771575 0.204072 ] mean value: 0.21480927467346192 key: score_time value: [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... 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[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.9s remaining: 1.8s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.9s remaining: 1.8s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.9s remaining: 1.9s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.0s remaining: 1.9s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.9s remaining: 1.9s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.0s remaining: 0.3s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.0s remaining: 1.9s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.0s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.0s remaining: 0.3s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.0s remaining: 1.9s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.0s remaining: 0.3s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.0s remaining: 2.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.0s remaining: 2.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.0s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.1s remaining: 0.4s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.1s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.0s remaining: 2.1s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. 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[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [0.06912875 0.05042171 0.0698061 0.04745865 0.06209087 0.04185343 0.06300735 0.04874206 0.07088113 0.06985283] mean value: 0.0593242883682251 key: test_mcc value: [0.8547619 0.8213423 0.95227002 0.86240942 0.8510645 0.80403025 0.75093926 1. 0.95118973 1. ] mean value: 0.8848007377234712 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.92682927 0.90909091 0.97674419 0.93333333 0.92682927 0.89473684 0.87804878 1. 0.97560976 1. ] mean value: 0.9421222343746749 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.9047619 0.83333333 0.95454545 0.875 0.9047619 0.94444444 0.85714286 1. 0.95238095 1. ] mean value: 0.9226370851370852 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.95 1. 1. 1. 0.95 0.85 0.9 1. 1. 1. ] mean value: 0.9650000000000001 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.92682927 0.90243902 0.97560976 0.92682927 0.925 0.9 0.875 1. 0.975 1. ] mean value: 0.9406707317073171 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.92738095 0.9047619 0.975 0.925 0.925 0.9 0.875 1. 0.975 1. ] mean value: 0.9407142857142856 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.86363636 0.83333333 0.95454545 0.875 0.86363636 0.80952381 0.7826087 1. 0.95238095 1. ] mean value: 0.893466497270845 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.12 MCC on Training: 0.88 Running classifier: 3 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.03535199 0.01816201 0.02092433 0.02148342 0.02034426 0.02129507 0.02234101 0.02256036 0.02219152 0.02153206] mean value: 0.022618603706359864 key: score_time value: [0.00858283 0.00850987 0.0085032 0.00851536 0.00854206 0.00864506 0.00853992 0.00849342 0.00854826 0.00854063] mean value: 0.008542060852050781 key: test_mcc value: [0.72229808 0.70272837 0.80817439 0.77831178 0.8510645 0.7 0.61237244 0.90453403 0.90453403 0.95118973] mean value: 0.7935207355950836 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.86363636 0.85106383 0.90909091 0.89361702 0.92682927 0.85 0.81818182 0.95238095 0.95238095 0.97560976] mean value: 0.899279087112507 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.79166667 0.74074074 0.86956522 0.80769231 0.9047619 0.85 0.75 0.90909091 0.90909091 0.95238095] mean value: 0.8484989607815695 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.95 1. 0.95238095 1. 0.95 0.85 0.9 1. 1. 1. ] mean value: 0.9602380952380951 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.85365854 0.82926829 0.90243902 0.87804878 0.925 0.85 0.8 0.95 0.95 0.975 ] mean value: 0.8913414634146342 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.85595238 0.83333333 0.90119048 0.875 0.925 0.85 0.8 0.95 0.95 0.975 ] mean value: 0.8915476190476191 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.76 0.74074074 0.83333333 0.80769231 0.86363636 0.73913043 0.69230769 0.90909091 0.90909091 0.95238095] mean value: 0.8207403643055816 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.18 MCC on Training: 0.79 Running classifier: 4 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.00939703 0.00963664 0.00949001 0.01050043 0.00953555 0.00958776 0.00937414 0.00924444 0.01063895 0.0101881 ] mean value: 0.009759306907653809 key: score_time value: [0.00856543 0.0087111 0.00961828 0.00884843 0.00875854 0.00856328 0.00863361 0.00921988 0.00891852 0.00920057] mean value: 0.008903765678405761 key: test_mcc value: [0.76500781 0.78072006 0.80817439 0.95227002 0.8510645 0.65081403 0.77459667 0.85972695 0.81649658 0.73379939] mean value: 0.7992670385058707 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.88372093 0.88888889 0.90909091 0.97674419 0.92682927 0.82926829 0.88888889 0.93023256 0.90909091 0.86956522] mean value: 0.9012320048745115 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.82608696 0.8 0.86956522 0.95454545 0.9047619 0.80952381 0.8 0.86956522 0.83333333 0.76923077] mean value: 0.843661266269962 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.95 1. 0.95238095 1. 0.95 0.85 1. 1. 1. 1. ] mean value: 0.9702380952380952 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.87804878 0.87804878 0.90243902 0.97560976 0.925 0.825 0.875 0.925 0.9 0.85 ] mean value: 0.8934146341463414 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.8797619 0.88095238 0.90119048 0.975 0.925 0.825 0.875 0.925 0.9 0.85 ] mean value: 0.8936904761904761 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.79166667 0.8 0.83333333 0.95454545 0.86363636 0.70833333 0.8 0.86956522 0.83333333 0.76923077] mean value: 0.8223644471470559 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.01 MCC on Training: 0.8 Running classifier: 5 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.11714005 0.11872625 0.12158871 0.12057233 0.1197474 0.11579919 0.11720061 0.11797214 0.1135664 0.10969043] mean value: 0.11720035076141358 key: score_time value: [0.02014589 0.01951933 0.01878238 0.01825404 0.0200305 0.01929593 0.01745963 0.01905465 0.01735091 0.0186646 ] mean value: 0.018855786323547362 key: test_mcc value: [0.90238095 0.90692382 0.85441771 0.90649828 0.9 0.7 0.90453403 0.95118973 1. 1. ] mean value: 0.9025944528502935 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.95 0.95238095 0.93023256 0.95454545 0.95 0.85 0.94736842 0.97560976 1. 1. ] mean value: 0.9510137142216134 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.95 0.90909091 0.90909091 0.91304348 0.95 0.85 1. 0.95238095 1. 1. ] mean value: 0.943360624882364 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.95 1. 0.95238095 1. 0.95 0.85 0.9 1. 1. 1. ] mean value: 0.9602380952380951 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.95121951 0.95121951 0.92682927 0.95121951 0.95 0.85 0.95 0.975 1. 1. ] mean value: 0.9505487804878048 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.95119048 0.95238095 0.92619048 0.95 0.95 0.85 0.95 0.975 1. 1. ] mean value: 0.9504761904761905 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.9047619 0.90909091 0.86956522 0.91304348 0.9047619 0.73913043 0.9 0.95238095 1. 1. ] mean value: 0.9092734801430453 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.04 MCC on Training: 0.9 Running classifier: 6 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.51702785 0.53877568 0.54382873 0.50591731 0.50111723 0.49906111 0.50231147 0.50407195 0.50725508 0.50231123] mean value: 0.5121677637100219 key: score_time value: [0.00946879 0.01007938 0.00984693 0.00921679 0.00922918 0.00916362 0.00939775 0.0092659 0.00935173 0.00918007] mean value: 0.009420013427734375 key: test_mcc value: [0.8547619 0.8213423 0.95227002 0.77831178 0.75858261 0.90453403 0.65743826 0.95118973 0.90453403 1. ] mean value: 0.8582964666413119 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.92682927 0.90909091 0.97674419 0.89361702 0.88372093 0.95238095 0.8372093 0.97560976 0.95238095 1. ] mean value: 0.9307583278124305 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.9047619 0.83333333 0.95454545 0.80769231 0.82608696 0.90909091 0.7826087 0.95238095 0.90909091 1. ] mean value: 0.8879591423069684 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.95 1. 1. 1. 0.95 1. 0.9 1. 1. 1. ] mean value: 0.9800000000000001 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.92682927 0.90243902 0.97560976 0.87804878 0.875 0.95 0.825 0.975 0.95 1. ] mean value: 0.9257926829268293 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.92738095 0.9047619 0.975 0.875 0.875 0.95 0.825 0.975 0.95 1. ] mean value: 0.9257142857142856 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.86363636 0.83333333 0.95454545 0.80769231 0.79166667 0.90909091 0.72 0.95238095 0.90909091 1. ] mean value: 0.8741436896436896 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.23 MCC on Training: 0.86 Running classifier: 7 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.01057816 0.00960398 0.00967455 0.01028872 0.00981617 0.01021457 0.00946283 0.01015353 0.00919318 0.01039839] mean value: 0.009938406944274902 key: score_time value: [0.00962329 0.0088551 0.00906706 0.00932527 0.00924969 0.00938725 0.00869775 0.00898123 0.0089829 0.00885963] mean value: 0.009102916717529297 key: test_mcc value: [0.31960727 0.31980998 0.56836003 0.46300848 0.51031036 0.55068879 0.45056356 0.35400522 0.40201513 0.25031309] mean value: 0.418868190595329 key: train_mcc value: [0.50412847 0.54269755 0.48255467 0.44758024 0.53875438 0.51161493 0.51105844 0.52747253 0.53655071 0.50659687] mean value: 0.5109008800663648 key: test_fscore value: [0.61111111 0.66666667 0.76923077 0.74418605 0.77272727 0.7804878 0.73170732 0.64864865 0.68421053 0.63414634] mean value: 0.704312250462652 key: train_fscore value: [0.75274725 0.77260274 0.71471471 0.70029674 0.76536313 0.74929577 0.7534626 0.76373626 0.75218659 0.74431818] mean value: 0.7468723984586391 key: test_precision value: [0.6875 0.63636364 0.83333333 0.72727273 0.70833333 0.76190476 0.71428571 0.70588235 0.72222222 0.61904762] mean value: 0.7116145700704525 key: train_precision value: [0.75274725 0.7704918 0.78289474 0.75641026 0.77840909 0.76878613 0.75977654 0.76373626 0.80124224 0.77058824] mean value: 0.7705082538723099 key: test_recall value: [0.55 0.7 0.71428571 0.76190476 0.85 0.8 0.75 0.6 0.65 0.65 ] mean value: 0.7026190476190477 key: train_recall value: [0.75274725 0.77472527 0.65745856 0.6519337 0.75274725 0.73076923 0.74725275 0.76373626 0.70879121 0.71978022] mean value: 0.7259941715742821 key: test_accuracy value: [0.65853659 0.65853659 0.7804878 0.73170732 0.75 0.775 0.725 0.675 0.7 0.625 ] mean value: 0.7079268292682926 key: train_accuracy value: [0.75206612 0.77134986 0.73829201 0.72176309 0.76923077 0.75549451 0.75549451 0.76373626 0.76648352 0.75274725] mean value: 0.7546657887566979 key: test_roc_auc value: [0.65595238 0.65952381 0.78214286 0.73095238 0.75 0.775 0.725 0.675 0.7 0.625 ] mean value: 0.7078571428571429 key: train_roc_auc value: [0.75206423 0.77134054 0.73806994 0.72157125 0.76923077 0.75549451 0.75549451 0.76373626 0.76648352 0.75274725] mean value: 0.754623277275211 key: test_jcc value: [0.44 0.5 0.625 0.59259259 0.62962963 0.64 0.57692308 0.48 0.52 0.46428571] mean value: 0.5468431013431012 key: train_jcc value: [0.60352423 0.62946429 0.55607477 0.53881279 0.6199095 0.5990991 0.60444444 0.61777778 0.60280374 0.59276018] mean value: 0.596467080942944 MCC on Blind test: 0.21 MCC on Training: 0.42 Running classifier: 8 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.0843761 0.1211338 0.12968946 0.11957812 0.11637235 0.1171174 0.120013 0.05942345 0.09034061 0.10233641] mean value: 0.10603806972503663 key: score_time value: [0.02177358 0.02188182 0.0249269 0.02540135 0.02161217 0.02984095 0.02467728 0.01439762 0.02261853 0.02456975] mean value: 0.023169994354248047 key: test_mcc value: [0.6133669 0.72229808 0.85441771 0.85441771 0.75858261 0.85972695 0.8 0.90453403 0.65743826 0.85972695] mean value: 0.7884509205409127 key: train_mcc value: [0.95076998 0.95644137 0.96215201 0.96180012 0.96755889 0.96225045 0.9569689 0.97289468 0.95627494 0.96755889] mean value: 0.9614670244833212 key: test_fscore value: [0.80952381 0.86363636 0.93023256 0.93023256 0.88372093 0.93023256 0.9 0.95238095 0.8372093 0.93023256] mean value: 0.8967401590657404 key: train_fscore value: [0.97560976 0.97837838 0.98102981 0.98092643 0.98378378 0.98113208 0.97849462 0.98644986 0.97826087 0.98378378] mean value: 0.9807849376050795 key: test_precision value: [0.77272727 0.79166667 0.90909091 0.90909091 0.82608696 0.86956522 0.9 0.90909091 0.7826087 0.86956522] mean value: 0.8539492753623188 key: train_precision value: [0.96256684 0.96276596 0.96276596 0.96774194 0.96808511 0.96296296 0.95789474 0.97326203 0.96774194 0.96808511] mean value: 0.9653872575437731 key: test_recall value: [0.85 0.95 0.95238095 0.95238095 0.95 1. 0.9 1. 0.9 1. ] mean value: 0.9454761904761904 key: train_recall value: [0.98901099 0.99450549 1. 0.99447514 1. 1. 1. 1. 0.98901099 1. ] mean value: 0.9967002610649021 key: test_accuracy value: [0.80487805 0.85365854 0.92682927 0.92682927 0.875 0.925 0.9 0.95 0.825 0.925 ] mean value: 0.8912195121951221 key: train_accuracy value: [0.97520661 0.97796143 0.98071625 0.98071625 0.98351648 0.98076923 0.97802198 0.98626374 0.97802198 0.98351648] mean value: 0.9804710441074077 key: test_roc_auc value: [0.80595238 0.85595238 0.92619048 0.92619048 0.875 0.925 0.9 0.95 0.825 0.925 ] mean value: 0.8914285714285715 key: train_roc_auc value: [0.97516848 0.97791573 0.98076923 0.98075405 0.98351648 0.98076923 0.97802198 0.98626374 0.97802198 0.98351648] mean value: 0.980471738206545 key: test_jcc value: [0.68 0.76 0.86956522 0.86956522 0.79166667 0.86956522 0.81818182 0.90909091 0.72 0.86956522] mean value: 0.8157200263504611 key: train_jcc value: [0.95238095 0.95767196 0.96276596 0.96256684 0.96808511 0.96296296 0.95789474 0.97326203 0.95744681 0.96808511] mean value: 0.9623122465586731 MCC on Blind test: -0.02 MCC on Training: 0.79 Running classifier: 9 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.01237535 0.01006484 0.00921226 0.00895238 0.00942707 0.01030731 0.00996494 0.00998354 0.00986862 0.0096972 ] mean value: 0.0099853515625 key: score_time value: [0.03746963 0.0136044 0.01534772 0.01399016 0.01431608 0.01471138 0.01443219 0.01284456 0.01568627 0.01378202] mean value: 0.01661844253540039 key: test_mcc value: [0.22195767 0.56836003 0.56086079 0.8047619 0.65465367 0.65743826 0.65465367 0.35043832 0.50251891 0.45056356] mean value: 0.5426206782251518 key: train_mcc value: [0.71870365 0.69354261 0.71240267 0.72012393 0.71796416 0.72168784 0.68867858 0.67528135 0.71058495 0.707582 ] mean value: 0.7066551741736788 key: test_fscore value: [0.61904762 0.79069767 0.79069767 0.9047619 0.83333333 0.8372093 0.83333333 0.66666667 0.76190476 0.73170732] mean value: 0.776935958728358 key: train_fscore value: [0.86528497 0.85421995 0.8622449 0.86582278 0.86513995 0.86666667 0.85213033 0.84455959 0.86153846 0.859375 ] mean value: 0.8596982594332987 key: test_precision value: [0.59090909 0.73913043 0.77272727 0.9047619 0.71428571 0.7826087 0.71428571 0.68421053 0.72727273 0.71428571] mean value: 0.7344477795278712 key: train_precision value: [0.81862745 0.79904306 0.80094787 0.79906542 0.8056872 0.8125 0.78341014 0.79901961 0.80769231 0.81683168] mean value: 0.8042824741784754 key: test_recall value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( [0.65 0.85 0.80952381 0.9047619 1. 0.9 1. 0.65 0.8 0.75 ] mean value: 0.8314285714285713 key: train_recall value: [0.91758242 0.91758242 0.93370166 0.94475138 0.93406593 0.92857143 0.93406593 0.8956044 0.92307692 0.90659341] mean value: 0.923559589581689 key: test_accuracy value: [0.6097561 0.7804878 0.7804878 0.90243902 0.8 0.825 0.8 0.675 0.75 0.725 ] mean value: 0.7648170731707318 key: train_accuracy value: [0.85674931 0.84297521 0.85123967 0.85399449 0.8543956 0.85714286 0.83791209 0.83516484 0.85164835 0.85164835] mean value: 0.8492870765598038 key: test_roc_auc value: [0.61071429 0.78214286 0.7797619 0.90238095 0.8 0.825 0.8 0.675 0.75 0.725 ] mean value: 0.7649999999999999 key: train_roc_auc value: [0.85658126 0.84276911 0.85146621 0.85424382 0.8543956 0.85714286 0.83791209 0.83516484 0.85164835 0.85164835] mean value: 0.8492972497116144 key: test_jcc value: [0.44827586 0.65384615 0.65384615 0.82608696 0.71428571 0.72 0.71428571 0.5 0.61538462 0.57692308] mean value: 0.6422934247162132 key: train_jcc value: [0.76255708 0.74553571 0.75784753 0.76339286 0.76233184 0.76470588 0.74235808 0.7309417 0.75675676 0.75342466] mean value: 0.753985210053389 MCC on Blind test: -0.0 MCC on Training: 0.54 Running classifier: 10 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.03777361 0.07431483 0.0382514 0.0381887 0.07334185 0.04971099 0.03855705 0.04069328 0.08022213 0.05326414] mean value: 0.052431797981262206 key: score_time value: [0.02398682 0.01215219 0.0120995 0.01215506 0.02422762 0.01248384 0.01217628 0.02320337 0.02063656 0.01201749] mean value: 0.016513872146606445 key: test_mcc value: [0.6806903 0.74124932 0.7197263 0.63496528 0.67131711 0.65743826 0.56803756 0.70352647 0.61237244 0.67131711] mean value: 0.666064014670832 key: train_mcc value: [0.89640477 0.8806926 0.88540137 0.86910742 0.87586358 0.87933328 0.88569897 0.86944527 0.8849494 0.88103803] mean value: 0.8807934682990523 key: test_fscore value: [0.84444444 0.86956522 0.86956522 0.83333333 0.84444444 0.8372093 0.8 0.85714286 0.81818182 0.84444444] mean value: 0.8418331079099532 key: train_fscore value: [0.94906166 0.94148936 0.94339623 0.93548387 0.93899204 0.94021739 0.94369973 0.93582888 0.94308943 0.94148936] mean value: 0.9412747956533292 key: test_precision value: [0.76 0.76923077 0.8 0.74074074 0.76 0.7826087 0.72 0.81818182 0.75 0.76 ] mean value: 0.7660762023805502 key: train_precision value: [0.92670157 0.91237113 0.92105263 0.91099476 0.90769231 0.93010753 0.92146597 0.91145833 0.93048128 0.91237113] mean value: 0.9184696654614927 key: test_recall value: [0.95 1. 0.95238095 0.95238095 0.95 0.9 0.9 0.9 0.9 0.95 ] mean value: 0.9354761904761905 key: train_recall value: [0.97252747 0.97252747 0.96685083 0.96132597 0.97252747 0.95054945 0.96703297 0.96153846 0.95604396 0.97252747] mean value: 0.9653451520854835 key: test_accuracy value: [0.82926829 0.85365854 0.85365854 0.80487805 0.825 0.825 0.775 0.85 0.8 0.825 ] mean value: 0.8241463414634147 key: train_accuracy value: [0.9476584 0.93939394 0.94214876 0.9338843 0.93681319 0.93956044 0.94230769 0.93406593 0.94230769 0.93956044] mean value: 0.9397700784064421 key: test_roc_auc value: [0.83214286 0.85714286 0.85119048 0.80119048 0.825 0.825 0.775 0.85 0.8 0.825 ] mean value: 0.8241666666666667 key: train_roc_auc value: [0.9475897 0.93930241 0.94221662 0.93395969 0.93681319 0.93956044 0.94230769 0.93406593 0.94230769 0.93956044] mean value: 0.9397683807904802 key: test_jcc value: [0.73076923 0.76923077 0.76923077 0.71428571 0.73076923 0.72 0.66666667 0.75 0.69230769 0.73076923] mean value: 0.7274029304029304 key: train_jcc value: [0.90306122 0.88944724 0.89285714 0.87878788 0.885 0.88717949 0.89340102 0.87939698 0.89230769 0.88944724] mean value: 0.8890885898136857 MCC on Blind test: -0.13 MCC on Training: 0.67 Running classifier: 11 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.04731345 0.03599572 0.03700066 0.08746815 0.05580997 0.05548787 0.04312682 0.03702617 0.03811216 0.03712344] mean value: 0.04744644165039062 key: score_time value: [0.01190948 0.01189756 0.01179385 0.01215005 0.01215172 0.0120039 0.01187396 0.01205397 0.01204395 0.01209402] mean value: 0.011997246742248535 key: test_mcc value: [0.56527676 0.70714286 0.61152662 0.7197263 0.75858261 0.70352647 0.71443451 0.40824829 0.45514956 0.55629391] mean value: 0.619990788815598 key: train_mcc value: [0.76335538 0.7469398 0.71349645 0.74106004 0.73666416 0.7363082 0.69230769 0.72531852 0.73078026 0.73078026] mean value: 0.7317010758611231 key: test_fscore value: [0.75675676 0.85 0.81818182 0.86956522 0.88372093 0.85714286 0.86363636 0.66666667 0.7027027 0.79069767] mean value: 0.8059070987129633 key: train_fscore value: [0.88346883 0.87567568 0.85635359 0.87052342 0.87027027 0.86885246 0.84615385 0.86187845 0.86575342 0.86501377] mean value: 0.8663943744743607 key: test_precision value: [0.82352941 0.85 0.7826087 0.8 0.82608696 0.81818182 0.79166667 0.75 0.76470588 0.73913043] mean value: 0.7945909865922653 key: train_precision value: [0.87165775 0.86170213 0.85635359 0.86813187 0.85638298 0.86413043 0.84615385 0.86666667 0.86338798 0.86740331] mean value: 0.8621970560348089 key: test_recall value: [0.7 0.85 0.85714286 0.95238095 0.95 0.9 0.95 0.6 0.65 0.85 ] mean value: 0.825952380952381 key: train_recall value: [0.8956044 0.89010989 0.85635359 0.87292818 0.88461538 0.87362637 0.84615385 0.85714286 0.86813187 0.86263736] mean value: 0.870730374597778 key: test_accuracy value: [0.7804878 0.85365854 0.80487805 0.85365854 0.875 0.85 0.85 0.7 0.725 0.775 ] mean value: 0.8067682926829269 key: train_accuracy value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( [0.8815427 0.87327824 0.85674931 0.87052342 0.86813187 0.86813187 0.84615385 0.86263736 0.86538462 0.86538462] mean value: 0.865791783973602 key: test_roc_auc value: [0.77857143 0.85357143 0.80357143 0.85119048 0.875 0.85 0.85 0.7 0.725 0.775 ] mean value: 0.8061904761904763 key: train_roc_auc value: [0.88150386 0.87323174 0.85674822 0.87053002 0.86813187 0.86813187 0.84615385 0.86263736 0.86538462 0.86538462] mean value: 0.8657838018335257 key: test_jcc value: [0.60869565 0.73913043 0.69230769 0.76923077 0.79166667 0.75 0.76 0.5 0.54166667 0.65384615] mean value: 0.6806544035674471 key: train_jcc value: [0.79126214 0.77884615 0.74879227 0.77073171 0.77033493 0.76811594 0.73333333 0.75728155 0.76328502 0.76213592] mean value: 0.7644118971091687 MCC on Blind test: 0.07 MCC on Training: 0.62 Running classifier: 12 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(cv=3, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(cv=3, random_state=42))]) key: fit_time value: [0.48068261 0.47225761 0.59419847 0.60979366 0.47439432 0.47586155 0.48258853 0.62551332 0.52078009 0.47545934] mean value: 0.5211529493331909 key: score_time value: [0.01216722 0.01217365 0.01213861 0.01214027 0.01212764 0.01195526 0.01204085 0.01203537 0.01201224 0.01195741] mean value: 0.012074851989746093 key: test_mcc value: [0.80907152 0.8213423 0.77831178 0.81975606 0.71443451 0.77459667 0.85972695 0.81649658 0.85972695 0.90453403] mean value: 0.8157997360394944 key: train_mcc value: [1. 0.97281876 0.97282283 0.9674736 0.98365012 0.99452051 0.98907071 0.97825827 0.96726661 0.96755889] mean value: 0.9793440307647021 key: test_fscore value: [0.9047619 0.90909091 0.89361702 0.91304348 0.86363636 0.88888889 0.93023256 0.90909091 0.93023256 0.95238095] mean value: 0.9094975543666463 key: train_fscore value: [1. 0.98644986 0.98637602 0.98369565 0.99182561 0.99726027 0.99453552 0.98913043 0.98369565 0.98378378] mean value: 0.9896752815388533 key: test_precision value: [0.86363636 0.83333333 0.80769231 0.84 0.79166667 0.8 0.86956522 0.83333333 0.86956522 0.90909091] mean value: 0.8417883348535522 key: train_precision value: [1. 0.97326203 0.97311828 0.96791444 0.98378378 0.99453552 0.98913043 0.97849462 0.97311828 0.96808511] mean value: 0.980144249745899 key: test_recall value: [0.95 1. 1. 1. 0.95 1. 1. 1. 1. 1. ] mean value: 0.99 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 0.99450549 1. ] mean value: 0.9994505494505495 key: test_accuracy value: [0.90243902 0.90243902 0.87804878 0.90243902 0.85 0.875 0.925 0.9 0.925 0.95 ] mean value: 0.9010365853658536 key: train_accuracy value: [1. 0.9862259 0.9862259 0.98347107 0.99175824 0.99725275 0.99450549 0.98901099 0.98351648 0.98351648] mean value: 0.9895483304574213 key: test_roc_auc value: [0.90357143 0.9047619 0.875 0.9 0.85 0.875 0.925 0.9 0.925 0.95 ] mean value: 0.9008333333333333 key: train_roc_auc value: [1. 0.98618785 0.98626374 0.98351648 0.99175824 0.99725275 0.99450549 0.98901099 0.98351648 0.98351648] mean value: 0.9895528504644527 key: test_jcc value: [0.82608696 0.83333333 0.80769231 0.84 0.76 0.8 0.86956522 0.83333333 0.86956522 0.90909091] mean value: 0.8348667274754231 key: train_jcc value: [1. 0.97326203 0.97311828 0.96791444 0.98378378 0.99453552 0.98913043 0.97849462 0.96791444 0.96808511] mean value: 0.979623865639177 MCC on Blind test: 0.03 MCC on Training: 0.82 Running classifier: 13 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [1.60645175 1.71215534 1.78610992 1.76761866 1.63174248 1.75476241 1.74219489 1.94464612 1.75362897 1.4540298 ] mean value: 1.7153340339660645 key: score_time value: [0.01627398 0.01446414 0.01454329 0.01466203 0.01807141 0.01460934 0.01430321 0.01535034 0.01247692 0.01261926] mean value: 0.014737391471862793 key: test_mcc value: [0.90238095 0.8213423 0.80817439 0.73786479 0.75858261 0.85972695 0.75093926 0.81649658 0.81649658 0.85972695] mean value: 0.8131731368870408 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.95 0.90909091 0.90909091 0.875 0.88372093 0.93023256 0.87804878 0.90909091 0.90909091 0.93023256] mean value: 0.908359846336307 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.95 0.83333333 0.86956522 0.77777778 0.82608696 0.86956522 0.85714286 0.83333333 0.83333333 0.86956522] mean value: 0.8519703243616286 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.95 1. 0.95238095 1. 0.95 1. 0.9 1. 1. 1. ] mean value: 0.9752380952380951 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.95121951 0.90243902 0.90243902 0.85365854 0.875 0.925 0.875 0.9 0.9 0.925 ] mean value: 0.9009756097560976 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.95119048 0.9047619 0.90119048 0.85 0.875 0.925 0.875 0.9 0.9 0.925 ] mean value: 0.9007142857142858 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.9047619 0.83333333 0.83333333 0.77777778 0.79166667 0.86956522 0.7826087 0.83333333 0.83333333 0.86956522] mean value: 0.8329278812974465 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.03 MCC on Training: 0.81 Running classifier: 14 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.01289916 0.01292944 0.01067924 0.00986457 0.00985742 0.00944567 0.01031256 0.01041245 0.00916529 0.01023722] mean value: 0.010580301284790039 key: score_time value: [0.01169872 0.01077366 0.00949931 0.00868392 0.00922608 0.00937963 0.00885844 0.00884056 0.00951076 0.00947452] mean value: 0.009594559669494629 key: test_mcc value: [0.07005567 0.36515617 0.65871309 0.46623254 0.55629391 0.60302269 0.40201513 0.4 0.55068879 0.30151134] mean value: 0.43736893351817707 key: train_mcc value: [0.47657934 0.53172821 0.54274958 0.45640803 0.56635312 0.50659687 0.49450549 0.50061246 0.50625927 0.58799197] mean value: 0.5169784355643039 key: test_fscore value: [0.48648649 0.66666667 0.8372093 0.75555556 0.79069767 0.78947368 0.71428571 0.7 0.76923077 0.66666667] mean value: 0.7176272519846572 key: train_fscore value: [0.73972603 0.76839237 0.76880223 0.71304348 0.78706199 0.74431818 0.74725275 0.74366197 0.74576271 0.79564033] mean value: 0.7553662038993556 key: test_precision value: [0.52941176 0.68421053 0.81818182 0.70833333 0.73913043 0.83333333 0.68181818 0.7 0.78947368 0.63636364] mean value: 0.712025671304511 key: train_precision value: [0.73770492 0.76216216 0.7752809 0.75 0.77248677 0.77058824 0.74725275 0.76300578 0.76744186 0.78918919] mean value: 0.7635112564106116 key: test_recall value: [0.45 0.65 0.85714286 0.80952381 0.85 0.75 0.75 0.7 0.75 0.7 ] mean value: 0.7266666666666667 key: train_recall value: [0.74175824 0.77472527 0.76243094 0.67955801 0.8021978 0.71978022 0.74725275 0.72527473 0.72527473 0.8021978 ] mean value: 0.7480450488737782 key: test_accuracy value: [0.53658537 0.68292683 0.82926829 0.73170732 0.775 0.8 0.7 0.7 0.775 0.65 ] mean value: 0.718048780487805 key: train_accuracy value: [0.73829201 0.76584022 0.77134986 0.72727273 0.78296703 0.75274725 0.74725275 0.75 0.75274725 0.79395604] mean value: 0.7582425150606968 key: test_roc_auc value: [0.53452381 0.68214286 0.82857143 0.7297619 0.775 0.8 0.7 0.7 0.775 0.65 ] mean value: 0.7175 key: train_roc_auc value: [0.73828244 0.76581568 0.77132536 0.72714164 0.78296703 0.75274725 0.74725275 0.75 0.75274725 0.79395604] mean value: 0.7582235444113897 key: test_jcc value: [0.32142857 0.5 0.72 0.60714286 0.65384615 0.65217391 0.55555556 0.53846154 0.625 0.5 ] mean value: 0.5673608589478155 key: train_jcc value: [0.58695652 0.62389381 0.62443439 0.55405405 0.64888889 0.59276018 0.59649123 0.59192825 0.59459459 0.66063348] mean value: 0.6074635398076297 MCC on Blind test: 0.09 MCC on Training: 0.44 Running classifier: 15 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.00932813 0.00944233 0.0091424 0.00959516 0.00938797 0.00951719 0.0092442 0.00928617 0.00999784 0.00932765] mean value: 0.00942690372467041 key: score_time value: [0.00850821 0.00847554 0.00866151 0.00852013 0.00856924 0.00859046 0.00892425 0.00865149 0.00848961 0.00871921] mean value: 0.008610963821411133 key: test_mcc value: [0.53206577 0.24110919 0.39966297 0.36666667 0.61237244 0.51031036 0.5 0.43643578 0.15491933 0.36147845] mean value: 0.41150209482696365 key: train_mcc value: [0.55077063 0.50646959 0.51430356 0.50942722 0.50286535 0.51011279 0.54849551 0.53544089 0.49177333 0.53530338] mean value: 0.5204962251655327 key: test_fscore value: [0.70588235 0.46666667 0.53333333 0.68292683 0.77777778 0.72222222 0.75 0.625 0.51428571 0.62857143] mean value: 0.6406666325066611 key: train_fscore value: [0.7318612 0.68646865 0.70322581 0.69902913 0.70063694 0.69281046 0.73520249 0.72151899 0.68403909 0.69565217] mean value: 0.7050444919874097 key: test_precision value: [0.85714286 0.7 0.88888889 0.7 0.875 0.8125 0.75 0.83333333 0.6 0.73333333] mean value: 0.7750198412698412 key: train_precision value: [0.85925926 0.85950413 0.84496124 0.84375 0.83333333 0.85483871 0.84892086 0.85074627 0.84 0.88888889] mean value: 0.8524202695666453 key: test_recall value: [0.6 0.35 0.38095238 0.66666667 0.7 0.65 0.75 0.5 0.45 0.55 ] mean value: 0.5597619047619047 key: train_recall value: [0.63736264 0.57142857 0.60220994 0.59668508 0.6043956 0.58241758 0.64835165 0.62637363 0.57692308 0.57142857] mean value: 0.6017576346305628 key: test_accuracy value: [0.75609756 0.6097561 0.65853659 0.68292683 0.8 0.75 0.75 0.7 0.575 0.675 ] mean value: 0.6957317073170731 key: train_accuracy value: [0.76584022 0.73829201 0.74655647 0.74380165 0.74175824 0.74175824 0.76648352 0.75824176 0.73351648 0.75 ] mean value: 0.7486248599884963 key: test_roc_auc value: [0.75238095 0.60357143 0.66547619 0.68333333 0.8 0.75 0.75 0.7 0.575 0.675 ] mean value: 0.6954761904761905 key: train_roc_auc value: [0.76619513 0.73875296 0.74615992 0.74339749 0.74175824 0.74175824 0.76648352 0.75824176 0.73351648 0.75 ] mean value: 0.7486263736263736 key: test_jcc value: [0.54545455 0.30434783 0.36363636 0.51851852 0.63636364 0.56521739 0.6 0.45454545 0.34615385 0.45833333] mean value: 0.47925709153970014 key: train_jcc value: [0.57711443 0.52261307 0.54228856 0.53731343 0.53921569 0.53 0.58128079 0.56435644 0.51980198 0.53333333] mean value: 0.5447317706863848 MCC on Blind test: -0.03 MCC on Training: 0.41 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=12, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.01359749 0.02263284 0.01903892 0.01861763 0.01954556 0.01828313 0.02097964 0.02080178 0.02100873 0.01862574] mean value: 0.01931314468383789 key: score_time value: [0.00895047 0.01196551 0.01187539 0.01183486 0.01179314 0.01186252 0.01194 0.01196647 0.01200628 0.01189661] mean value: 0.011609125137329101 key: test_mcc value: [0.46428571 0.51754917 0.63496528 0.52420964 0.6289709 0.54554473 0.53881591 0.58713656 0.48038446 0.50390326] mean value: 0.5425765623667622 key: train_mcc value: [0.75198333 0.52887225 0.67737077 0.67371776 0.78406384 0.64285714 0.56889047 0.74161985 0.75426057 0.55742784] mean value: 0.6681063806690697 key: test_fscore value: [0.73170732 0.76923077 0.83333333 0.73684211 0.82608696 0.79166667 0.78431373 0.72727273 0.76595745 0.7755102 ] mean value: 0.7741921251741903 key: train_fscore value: [0.88101266 0.78111588 0.84433962 0.80126183 0.89528796 0.82949309 0.79649891 0.84662577 0.88161209 0.79464286] mean value: 0.8351890656625688 key: test_precision value: [0.71428571 0.625 0.74074074 0.82352941 0.73076923 0.67857143 0.64516129 0.92307692 0.66666667 0.65517241] mean value: 0.7202973819991094 key: train_precision value: [0.81690141 0.64084507 0.73662551 0.93382353 0.855 0.71428571 0.66181818 0.95833333 0.81395349 0.66917293] mean value: 0.7800759172828446 key: test_recall value: [0.75 1. 0.95238095 0.66666667 0.95 0.95 1. 0.6 0.9 0.95 ] mean value: 0.8719047619047618 key: train_recall value: [0.95604396 1. 0.98895028 0.70165746 0.93956044 0.98901099 1. 0.75824176 0.96153846 0.97802198] mean value: 0.9273025317224212 key: test_accuracy value: [0.73170732 0.70731707 0.80487805 0.75609756 0.8 0.75 0.725 0.775 0.725 0.725 ] mean value: 0.7499999999999999 key: train_accuracy value: [0.87052342 0.71900826 0.81818182 0.82644628 0.89010989 0.7967033 0.74450549 0.86263736 0.87087912 0.74725275] mean value: 0.8146247691702235 key: test_roc_auc value: [0.73214286 0.71428571 0.80119048 0.75833333 0.8 0.75 0.725 0.775 0.725 0.725 ] mean value: 0.750595238095238 key: train_roc_auc value: [0.87028717 0.71823204 0.81865096 0.82610345 0.89010989 0.7967033 0.74450549 0.86263736 0.87087912 0.74725275] mean value: 0.8145361544532814 key: test_jcc value: [0.57692308 0.625 0.71428571 0.58333333 0.7037037 0.65517241 0.64516129 0.57142857 0.62068966 0.63333333] mean value: 0.6329031092295831 key: train_jcc value: [0.78733032 0.64084507 0.73061224 0.66842105 0.81042654 0.70866142 0.66181818 0.73404255 0.78828829 0.65925926] mean value: 0.7189704924858568 MCC on Blind test: 0.0 MCC on Training: 0.54 Running classifier: 17 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.02917719 0.02969289 0.02809596 0.0269928 0.02680182 0.02734494 0.02650619 0.02614403 0.02765489 0.0265615 ] mean value: 0.027497220039367675 key: score_time value: [0.01323438 0.01315546 0.01277542 0.01320362 0.01273775 0.01300716 0.01299357 0.01263332 0.01288676 0.01294494] mean value: 0.012957239151000976 key: test_mcc value: [0.95227002 0.95238095 0.95238095 1. 0.95118973 0.80403025 0.90453403 1. 1. 1. ] mean value: 0.951678593709772 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.97435897 0.97560976 0.97560976 1. 0.97435897 0.89473684 0.94736842 1. 1. 1. ] mean value: 0.9742042724070966 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.95238095 1. 1. 1. 0.94444444 1. 1. 1. 1. ] mean value: 0.9896825396825397 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.95 1. 0.95238095 1. 0.95 0.85 0.9 1. 1. 1. ] mean value: 0.9602380952380951 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.97560976 0.97560976 0.97560976 1. 0.975 0.9 0.95 1. 1. 1. ] mean value: 0.9751829268292683 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.975 0.97619048 0.97619048 1. 0.975 0.9 0.95 1. 1. 1. ] mean value: 0.9752380952380953 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.95 0.95238095 0.95238095 1. 0.95 0.80952381 0.9 1. 1. 1. ] mean value: 0.9514285714285714 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.0 MCC on Training: 0.95 Running classifier: 18 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42))]) key: fit_time value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( [0.65076423 0.66926861 0.63418579 0.71304417 0.6594913 0.68460131 0.65814996 0.62556553 0.59677315 0.64587784] mean value: 0.6537721872329711 key: score_time value: [0.17696428 0.19411016 0.18363786 0.19860983 0.1588347 0.18311095 0.16452432 0.13824272 0.19541597 0.14329123] mean value: 0.17367420196533204 key: test_mcc value: [0.8547619 0.90692382 0.85441771 0.90649828 0.80403025 0.95118973 0.8 0.95118973 0.81649658 0.95118973] mean value: 0.8796697742708377 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.92682927 0.95238095 0.93023256 0.95454545 0.9047619 0.97560976 0.9 0.97560976 0.90909091 0.97560976] mean value: 0.940467031550412 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.9047619 0.90909091 0.90909091 0.91304348 0.86363636 0.95238095 0.9 0.95238095 0.83333333 0.95238095] mean value: 0.9090099755317148 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.95 1. 0.95238095 1. 0.95 1. 0.9 1. 1. 1. ] mean value: 0.9752380952380951 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.92682927 0.95121951 0.92682927 0.95121951 0.9 0.975 0.9 0.975 0.9 0.975 ] mean value: 0.938109756097561 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.92738095 0.95238095 0.92619048 0.95 0.9 0.975 0.9 0.975 0.9 0.975 ] mean value: 0.9380952380952381 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.86363636 0.90909091 0.86956522 0.91304348 0.82608696 0.95238095 0.81818182 0.95238095 0.83333333 0.95238095] mean value: 0.8890080933559193 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.21 MCC on Training: 0.88 Running classifier: 19 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea...age_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42))]) key: fit_time value: [0.98382998 1.00433731 0.94691682 1.02536702 0.96836805 0.9551518 0.99430156 0.95543933 0.98800635 1.04738259] mean value: 0.986910080909729 key: score_time value: [0.20908594 0.23797917 0.17769408 0.21423078 0.24258494 0.22254586 0.24202013 0.18882108 0.18830729 0.21323299] mean value: 0.21365022659301758 key: test_mcc value: [0.7565654 0.76500781 0.90238095 0.85441771 0.80403025 0.9 0.8 0.85972695 0.65081403 0.9 ] mean value: 0.8192943100383859 key: train_mcc value: [0.95054046 0.92849771 0.96700161 0.95054495 0.96190152 0.9505638 0.96190152 0.93429161 0.93957462 0.94511201] mean value: 0.9489929813045134 key: test_fscore value: [0.87179487 0.88372093 0.95238095 0.93023256 0.9047619 0.95 0.9 0.93023256 0.82926829 0.95 ] mean value: 0.9102392068132283 key: train_fscore value: [0.97547684 0.96457766 0.98351648 0.97534247 0.98102981 0.97534247 0.98102981 0.9673913 0.96969697 0.9726776 ] mean value: 0.9746081401205557 key: test_precision value: [0.89473684 0.82608696 0.95238095 0.90909091 0.86363636 0.95 0.9 0.86956522 0.80952381 0.95 ] mean value: 0.892502105065034 key: train_precision value: [0.96756757 0.95675676 0.97814208 0.9673913 0.96791444 0.9726776 0.96791444 0.95698925 0.97237569 0.9673913 ] mean value: 0.9675120420076034 key: test_recall value: [0.85 0.95 0.95238095 0.95238095 0.95 0.95 0.9 1. 0.85 0.95 ] mean value: 0.9304761904761903 key: train_recall value: [0.98351648 0.97252747 0.98895028 0.98342541 0.99450549 0.97802198 0.99450549 0.97802198 0.96703297 0.97802198] mean value: 0.9818529536761581 key: test_accuracy value: [0.87804878 0.87804878 0.95121951 0.92682927 0.9 0.95 0.9 0.925 0.825 0.95 ] mean value: 0.9084146341463413 key: train_accuracy value: [0.97520661 0.96418733 0.98347107 0.97520661 0.98076923 0.97527473 0.98076923 0.96703297 0.96978022 0.97252747] mean value: 0.9744225471498199 key: test_roc_auc value: [0.87738095 0.8797619 0.95119048 0.92619048 0.9 0.95 0.9 0.925 0.825 0.95 ] mean value: 0.9084523809523809 key: train_roc_auc value: [0.97518366 0.96416429 0.98348613 0.97522919 0.98076923 0.97527473 0.98076923 0.96703297 0.96978022 0.97252747] mean value: 0.9744217108857993 key: test_jcc value: [0.77272727 0.79166667 0.90909091 0.86956522 0.82608696 0.9047619 0.81818182 0.86956522 0.70833333 0.9047619 ] mean value: 0.8374741200828156 key: train_jcc value: [0.95212766 0.93157895 0.96756757 0.95187166 0.96276596 0.95187166 0.96276596 0.93684211 0.94117647 0.94680851] mean value: 0.9505376491401787 MCC on Blind test: 0.18 MCC on Training: 0.82 Running classifier: 20 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.0258472 0.01484084 0.0149467 0.03025317 0.03562021 0.01486349 0.01822519 0.03501558 0.03450966 0.03748584] mean value: 0.026160788536071778 key: score_time value: [0.01213741 0.01180077 0.01648664 0.02028465 0.01186991 0.01199341 0.02104664 0.01190877 0.02219367 0.02271533] mean value: 0.016243720054626466 key: test_mcc value: [0.61152662 0.65952381 0.7565654 0.65915306 0.6289709 0.65743826 0.70352647 0.40201513 0.65081403 0.70352647] mean value: 0.6433060143441589 key: train_mcc value: [0.81343877 0.80207179 0.81921279 0.80867933 0.82018072 0.842199 0.85269156 0.81441709 0.82497319 0.83118986] mean value: 0.8229054099734243 key: test_fscore value: [0.78947368 0.82926829 0.88372093 0.84 0.82608696 0.8372093 0.85714286 0.68421053 0.82926829 0.85714286] mean value: 0.8233523699257763 key: train_fscore value: [0.90860215 0.9027027 0.91105121 0.90616622 0.912 0.92266667 0.92761394 0.90909091 0.91397849 0.91733333] mean value: 0.9131205630750816 key: test_precision value: [0.83333333 0.80952381 0.86363636 0.72413793 0.73076923 0.7826087 0.81818182 0.72222222 0.80952381 0.81818182] mean value: 0.7912119032059063 key: train_precision value: [0.88947368 0.88829787 0.88947368 0.88020833 0.88601036 0.89637306 0.90575916 0.88541667 0.89473684 0.89119171] mean value: 0.8906941374704085 key: test_recall value: [0.75 0.85 0.9047619 1. 0.95 0.9 0.9 0.65 0.85 0.9 ] mean value: 0.8654761904761905 key: train_recall value: [0.92857143 0.91758242 0.93370166 0.93370166 0.93956044 0.95054945 0.95054945 0.93406593 0.93406593 0.94505495] mean value: 0.9367403314917127 key: test_accuracy value: [0.80487805 0.82926829 0.87804878 0.80487805 0.8 0.825 0.85 0.7 0.825 0.85 ] mean value: 0.8167073170731708 key: train_accuracy value: [0.90633609 0.90082645 0.90909091 0.90358127 0.90934066 0.92032967 0.92582418 0.90659341 0.91208791 0.91483516] mean value: 0.910884569975479 key: test_roc_auc value: [0.80357143 0.8297619 0.87738095 0.8 0.8 0.825 0.85 0.7 0.825 0.85 ] mean value: 0.8160714285714287 key: train_roc_auc value: [0.90627466 0.90078016 0.90915852 0.90366402 0.90934066 0.92032967 0.92582418 0.90659341 0.91208791 0.91483516] mean value: 0.9108888349219841 key: test_jcc value: [0.65217391 0.70833333 0.79166667 0.72413793 0.7037037 0.72 0.75 0.52 0.70833333 0.75 ] mean value: 0.7028348881114997 key: train_jcc value: [0.83251232 0.8226601 0.83663366 0.82843137 0.83823529 0.85643564 0.865 0.83333333 0.84158416 0.84729064] mean value: 0.8402116519533726 MCC on Blind test: -0.02 MCC on Training: 0.64 Running classifier: 21 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=3))]) key: fit_time value: [0.10774326 0.14563179 0.11255741 0.11489463 0.14222765 0.14285636 0.11114216 0.1094842 0.11216831 0.11159348] mean value: 0.12102992534637451 key: score_time value: [0.01363587 0.02207875 0.02282596 0.02308702 0.0239141 0.02383494 0.01766515 0.02357388 0.02177024 0.02361894] mean value: 0.02160048484802246 key: test_mcc value: [0.66668392 0.8213423 0.7633652 0.77831178 0.6289709 0.61237244 0.65743826 0.70352647 0.70352647 0.55629391] mean value: 0.689183164857558 key: train_mcc value: [0.86906802 0.87486488 0.86291442 0.84179277 0.85320814 0.88473559 0.85269156 0.85382927 0.85901208 0.84866842] mean value: 0.8600785157819548 key: test_fscore value: [0.8372093 0.90909091 0.88888889 0.89361702 0.82608696 0.81818182 0.8372093 0.85714286 0.85714286 0.79069767] mean value: 0.8515267587315434 key: train_fscore value: [0.93582888 0.93866667 0.93224932 0.92225201 0.928 0.94277929 0.92761394 0.92838196 0.93085106 0.92592593] mean value: 0.9312549062081434 key: test_precision value: [0.7826087 0.83333333 0.83333333 0.80769231 0.73076923 0.75 0.7826087 0.81818182 0.81818182 0.73913043] mean value: 0.7895839667578798 key: train_precision value: [0.91145833 0.9119171 0.91489362 0.89583333 0.9015544 0.93513514 0.90575916 0.8974359 0.90206186 0.89285714] mean value: 0.9068905979680559 key: test_recall value: [0.9 1. 0.95238095 1. 0.95 0.9 0.9 0.9 0.9 0.85 ] mean value: 0.9252380952380952 key: train_recall value: [0.96153846 0.96703297 0.95027624 0.95027624 0.95604396 0.95054945 0.95054945 0.96153846 0.96153846 0.96153846] mean value: 0.9570882156517515 key: test_accuracy value: [0.82926829 0.90243902 0.87804878 0.87804878 0.8 0.8 0.825 0.85 0.85 0.775 ] mean value: 0.8387804878048779 key: train_accuracy value: [0.9338843 0.93663912 0.93112948 0.92011019 0.92582418 0.94230769 0.92582418 0.92582418 0.92857143 0.92307692] mean value: 0.9293191656828022 key: test_roc_auc value: [0.83095238 0.9047619 0.87619048 0.875 0.8 0.8 0.825 0.85 0.85 0.775 ] mean value: 0.8386904761904761 key: train_roc_auc value: [0.9338079 0.93655516 0.93118208 0.92019307 0.92582418 0.94230769 0.92582418 0.92582418 0.92857143 0.92307692] mean value: 0.9293166777973407 key: test_jcc value: [0.72 0.83333333 0.8 0.80769231 0.7037037 0.69230769 0.72 0.75 0.75 0.65384615] mean value: 0.7430883190883191 key: train_jcc value: [0.87939698 0.88442211 0.87309645 0.85572139 0.86567164 0.89175258 0.865 0.86633663 0.87064677 0.86206897] mean value: 0.8714113519673117 MCC on Blind test: -0.16 MCC on Training: 0.69 Running classifier: 22 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.03523827 0.01954842 0.01814127 0.01879573 0.01790881 0.01916146 0.01794839 0.0164392 0.01867104 0.01745796] mean value: 0.01993105411529541 key: score_time value: [0.01677752 0.01162791 0.0117507 0.01215243 0.01222348 0.01192212 0.01200366 0.0111239 0.01227045 0.01191616] mean value: 0.012376832962036132 key: test_mcc value: [0.46623254 0.56086079 0.8047619 0.7098505 0.71443451 0.7 0.77459667 0.50251891 0.60302269 0.7 ] mean value: 0.6536278510607707 key: train_mcc value: [0.724887 0.69697338 0.71354847 0.70799308 0.73666416 0.69789702 0.70346662 0.7036792 0.76997498 0.71428571] mean value: 0.7169369601558414 key: test_fscore value: [0.7027027 0.76923077 0.9047619 0.86363636 0.86363636 0.85 0.88888889 0.73684211 0.78947368 0.85 ] mean value: 0.8219172782330677 key: train_fscore value: [0.86486486 0.84931507 0.85714286 0.8531856 0.87027027 0.84764543 0.85 0.84916201 0.88709677 0.85714286] mean value: 0.858582572821148 key: test_precision value: [0.76470588 0.78947368 0.9047619 0.82608696 0.79166667 0.85 0.8 0.77777778 0.83333333 0.85 ] mean value: 0.818780620562489 key: train_precision value: [0.85106383 0.84699454 0.85245902 0.85555556 0.85638298 0.8547486 0.85955056 0.86363636 0.86842105 0.85714286] mean value: 0.856595535453927 key: test_recall value: [0.65 0.75 0.9047619 0.9047619 0.95 0.85 1. 0.7 0.75 0.85 ] mean value: 0.830952380952381 key: train_recall value: [0.87912088 0.85164835 0.86187845 0.85082873 0.88461538 0.84065934 0.84065934 0.83516484 0.90659341 0.85714286] mean value: 0.8608311577924839 key: test_accuracy value: [0.73170732 0.7804878 0.90243902 0.85365854 0.85 0.85 0.875 0.75 0.8 0.85 ] mean value: 0.824329268292683 key: train_accuracy value: [0.86225895 0.84848485 0.85674931 0.85399449 0.86813187 0.8489011 0.85164835 0.85164835 0.88461538 0.85714286] mean value: 0.8583575515393698 key: test_roc_auc value: [0.7297619 0.7797619 0.90238095 0.85238095 0.85 0.85 0.875 0.75 0.8 0.85 ] mean value: 0.8239285714285713 key: train_roc_auc value: [0.86221237 0.84847611 0.8567634 0.85398579 0.86813187 0.8489011 0.85164835 0.85164835 0.88461538 0.85714286] mean value: 0.8583525590431668 key: test_jcc value: [0.54166667 0.625 0.82608696 0.76 0.76 0.73913043 0.8 0.58333333 0.65217391 0.73913043] mean value: 0.7026521739130435 key: train_jcc value: [0.76190476 0.73809524 0.75 0.74396135 0.77033493 0.73557692 0.73913043 0.73786408 0.79710145 0.75 ] mean value: 0.7523969165691466 MCC on Blind test: 0.05 MCC on Training: 0.65 Running classifier: 23 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.02435851 0.01802087 0.01892734 0.02119303 0.01979542 0.02172494 0.02183723 0.02126312 0.02239442 0.01882052] mean value: 0.020833539962768554 key: score_time value: [0.00896931 0.01243997 0.01245666 0.01258087 0.01270413 0.01262379 0.01266479 0.01275563 0.01274514 0.01258755] mean value: 0.01225278377532959 key: test_mcc value: [0.38139686 0.61152662 0.53206577 0.698212 0.41907904 0.61588176 0.43643578 0.57735027 0.69388867 0.55068879] mean value: 0.5516525551469371 key: train_mcc value: [0.47727457 0.78547402 0.73132428 0.81348392 0.61780851 0.56038453 0.62598077 0.6765539 0.69398669 0.74674584] mean value: 0.6729017016824029 key: test_fscore value: [0.5 0.78947368 0.79166667 0.85714286 0.74509804 0.81632653 0.75 0.66666667 0.85106383 0.7804878 ] mean value: 0.7547926079179931 key: train_fscore value: [0.5625 0.88304094 0.87088608 0.90810811 0.81922197 0.79385965 0.82469136 0.79487179 0.85176471 0.86297376] mean value: 0.8171918356527967 key: test_precision value: [0.875 0.83333333 0.7037037 0.75 0.61290323 0.68965517 0.64285714 1. 0.74074074 0.76190476] mean value: 0.7610098080759926 key: train_precision value: [0.97297297 0.94375 0.80373832 0.88888889 0.70196078 0.66058394 0.74887892 0.95384615 0.74485597 0.91925466] mean value: 0.8338730608614687 key: test_recall value: [0.35 0.75 0.9047619 1. 0.95 1. 0.9 0.5 1. 0.8 ] mean value: 0.8154761904761905 key: train_recall value: [0.3956044 0.82967033 0.95027624 0.9281768 0.98351648 0.99450549 0.91758242 0.68131868 0.99450549 0.81318681] mean value: 0.8488343148564145 key: test_accuracy value: [0.65853659 0.80487805 0.75609756 0.82926829 0.675 0.775 0.7 0.75 0.825 0.775 ] mean value: 0.7548780487804879 key: train_accuracy value: [0.69146006 0.88980716 0.85950413 0.90633609 0.78296703 0.74175824 0.80494505 0.82417582 0.82692308 0.87087912] mean value: 0.819875578966488 key: test_roc_auc value: [0.65119048 0.80357143 0.75238095 0.825 0.675 0.775 0.7 0.75 0.825 0.775 ] mean value: 0.7532142857142858 key: train_roc_auc value: [0.69227734 0.88997329 0.85975351 0.90639609 0.78296703 0.74175824 0.80494505 0.82417582 0.82692308 0.87087912] mean value: 0.8200048570214316 key: test_jcc value: [0.33333333 0.65217391 0.65517241 0.75 0.59375 0.68965517 0.6 0.5 0.74074074 0.64 ] mean value: 0.6154825573324448 key: train_jcc value: [0.39130435 0.79057592 0.77130045 0.83168317 0.69379845 0.65818182 0.70168067 0.65957447 0.74180328 0.75897436] mean value: 0.6998876926614896 MCC on Blind test: -0.22 MCC on Training: 0.55 Running classifier: 24 Model_name: XGBoost Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.1216054 0.08034754 0.08609152 0.08204103 0.08094382 0.08143473 0.07808423 0.0834744 0.09230685 0.08362055] mean value: 0.08699500560760498 key: score_time value: [0.0113976 0.01133084 0.01199317 0.0117743 0.01080441 0.01116228 0.01099825 0.01155901 0.01094103 0.01149893] mean value: 0.011345982551574707 key: test_mcc value: [0.8547619 0.90692382 0.90649828 0.90649828 0.80403025 0.95118973 0.70352647 1. 0.90453403 1. ] mean value: 0.8937962775247147 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.92682927 0.95238095 0.95454545 0.95454545 0.9047619 0.97560976 0.85714286 1. 0.95238095 1. ] mean value: 0.947819660014782 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.9047619 0.90909091 0.91304348 0.91304348 0.86363636 0.95238095 0.81818182 1. 0.90909091 1. ] /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:463: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_CV['source_data'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:490: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_BT['source_data'] = 'BT' mean value: 0.9183229813664596 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.95 1. 1. 1. 0.95 1. 0.9 1. 1. 1. ] mean value: 0.9800000000000001 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.92682927 0.95121951 0.95121951 0.95121951 0.9 0.975 0.85 1. 0.95 1. ] mean value: 0.9455487804878049 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.92738095 0.95238095 0.95 0.95 0.9 0.975 0.85 1. 0.95 1. ] mean value: 0.9454761904761904 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.86363636 0.90909091 0.91304348 0.91304348 0.82608696 0.95238095 0.75 1. 0.90909091 1. ] mean value: 0.9036373047242611 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.3 MCC on Training: 0.89 Extracting tts_split_name: 80_20 Total cols in each df: CV df: 8 metaDF: 17 Adding column: Model_name Total cols in bts df: BT_df: 8 First proceeding to rowbind CV and BT dfs: Final output should have: 25 columns Combinig 2 using pd.concat by row ~ rowbind Checking Dims of df to combine: Dim of CV: (24, 8) Dim of BT: (24, 8) 8 Number of Common columns: 8 These are: ['source_data', 'Precision', 'JCC', 'MCC', 'Recall', 'Accuracy', 'F1', 'ROC_AUC'] Concatenating dfs with different resampling methods [WF]: Split type: 80_20 No. of dfs combining: 2 PASS: 2 dfs successfully combined nrows in combined_df_wf: 48 ncols in combined_df_wf: 8 PASS: proceeding to merge metadata with CV and BT dfs Adding column: Model_name ========================================================= SUCCESS: Ran multiple classifiers ======================================================= ============================================================== Running several classification models (n): 24 List of models: ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('Gaussian NB', GaussianNB()) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('K-Nearest Neighbors', KNeighborsClassifier()) ('LDA', LinearDiscriminantAnalysis()) ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(cv=3, random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Multinomial', MultinomialNB()) ('Naive Bayes', BernoulliNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=12, random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Random Forest', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42)) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=3)) ('SVC', SVC(random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=12, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ================================================================ Running classifier: 1 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.09347725 0.09026599 0.08826327 0.08432293 0.08743382 0.08815742 0.08936024 0.08612823 0.09169912 0.08717275] mean value: 0.08862810134887696 key: score_time value: [0.01443744 0.01585865 0.01595235 0.01517844 0.0146513 0.0159812 0.01478386 0.01525879 0.01628637 0.01508236] mean value: 0.01534707546234131 key: test_mcc value: [ 0.40824829 0.2 0.6 0.21821789 0.6 0.21821789 0.6 -0.21821789 0.40824829 0. ] mean value: 0.30347144711637186 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.72727273 0.6 0.8 0.5 0.8 0.5 0.8 0.25 0.66666667 0.54545455] mean value: 0.6189393939393939 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.66666667 0.6 0.8 0.66666667 0.8 0.66666667 0.8 0.33333333 0.75 0.5 ] mean value: 0.6583333333333334 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.8 0.6 0.8 0.4 0.8 0.4 0.8 0.2 0.6 0.6] mean value: 0.6 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.7 0.6 0.8 0.6 0.8 0.6 0.8 0.4 0.7 0.5] mean value: 0.65 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.7 0.6 0.8 0.6 0.8 0.6 0.8 0.4 0.7 0.5] mean value: 0.6500000000000001 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.57142857 0.42857143 0.66666667 0.33333333 0.66666667 0.33333333 0.66666667 0.14285714 0.5 0.375 ] mean value: 0.46845238095238095 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.0 MCC on Training: 0.3 Running classifier: 2 Model_name: Bagging Classifier Model func: BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3) Running model pipeline: [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... `Ó'ÁžUá vçÀžU°WœºBuilding estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... ÿÿÿÿky_pÁ ìg=ºUl@0[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... ÿKt”bK…”ŒC”t”R”.”…”R”Kdt”}”(h:KŒ check_input”ˆu‡”ahŒThreadingBackend”“”)”}”(Œ nesting_level”KŒinner_max_num_threads”NubN†”N}”t”R”sbŒargs”)Œkwargs”}”Œ loky_pickler”Œ cloudpickle”u[Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.7s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.7s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.7s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.7s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.7s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.7s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.7s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.4s remaining: 0.7s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.4s remaining: 0.7s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.4s remaining: 0.8s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3))]) key: fit_time value: [0.10304475 0.14163876 0.15288591 0.17695999 0.16203403 0.16304851 0.16216993 0.14465809 0.12714624 0.14146757] mean value: 0.14750537872314454 key: score_time value: [0.0781846 0.04545355 0.07651615 0.0858593 0.0789175 0.04379892 0.06800199 0.03906131 0.08122754 0.04581666] mean value: 0.06428375244140624 key: test_mcc value: [0.65465367 0.65465367 0.81649658 0.21821789 0.21821789 0.21821789 0.81649658 0.21821789 0.21821789 0.2 ] mean value: 0.42333899544513687 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.83333333 0.83333333 0.88888889 0.5 0.66666667 0.5 0.88888889 0.5 0.66666667 0.6 ] mean value: 0.6877777777777777 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.71428571 0.71428571 1. 0.66666667 0.57142857 0.66666667 1. 0.66666667 0.57142857 0.6 ] mean value: 0.7171428571428571 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 0.8 0.4 0.8 0.4 0.8 0.4 0.8 0.6] mean value: 0.7 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8 0.8 0.9 0.6 0.6 0.6 0.9 0.6 0.6 0.6] mean value: 0.7 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.8 0.8 0.9 0.6 0.6 0.6 0.9 0.6 0.6 0.6] mean value: 0.7 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.71428571 0.71428571 0.8 0.33333333 0.5 0.33333333 0.8 0.33333333 0.5 0.42857143] mean value: 0.5457142857142857 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.07 MCC on Training: 0.42 Running classifier: 3 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.02662444 0.01178408 0.01207471 0.01194215 0.0117352 0.01172924 0.01127982 0.01201892 0.01202631 0.01070619] mean value: 0.013192105293273925 key: score_time value: [0.01134038 0.00970483 0.00964141 0.00946164 0.00967526 0.00973749 0.00938821 0.00935841 0.00921535 0.00874043] mean value: 0.009626340866088868 key: test_mcc value: [ 0.2 0.33333333 0.5 -0.21821789 0.6 0.21821789 0.33333333 0.21821789 0. 0.2 ] mean value: 0.23848845569026592 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.6 0.71428571 0.57142857 0.25 0.8 0.5 0.33333333 0.5 0.54545455 0.6 ] mean value: 0.5414502164502164 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.6 0.55555556 1. 0.33333333 0.8 0.66666667 1. 0.66666667 0.5 0.6 ] mean value: 0.6722222222222222 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.6 1. 0.4 0.2 0.8 0.4 0.2 0.4 0.6 0.6] mean value: 0.5199999999999999 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.6 0.6 0.7 0.4 0.8 0.6 0.6 0.6 0.5 0.6] mean value: 0.5999999999999999 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.6 0.6 0.7 0.4 0.8 0.6 0.6 0.6 0.5 0.6] mean value: 0.5999999999999999 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.42857143 0.55555556 0.4 0.14285714 0.66666667 0.33333333 0.2 0.33333333 0.375 0.42857143] mean value: 0.38638888888888884 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.04 MCC on Training: 0.24 Running classifier: 4 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.00920415 0.0088017 0.00824332 0.00843644 0.00820732 0.00824738 0.0082345 0.00820947 0.00832319 0.00838614] mean value: 0.008429360389709473 key: score_time value: [0.0098877 0.00827408 0.00844836 0.0081985 0.00829434 0.00820518 0.00822759 0.00818849 0.00827646 0.00839353] mean value: 0.008439421653747559 key: test_mcc value: [ 0. -0.33333333 0.2 0.5 0.5 0.40824829 0. 0. 0.6 0.21821789] mean value: 0.2093132847366522 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.54545455 0. 0.6 0.57142857 0.76923077 0.66666667 0.54545455 0.28571429 0.8 0.66666667] mean value: 0.5450616050616051 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.5 0. 0.6 1. 0.625 0.75 0.5 0.5 0.8 0.57142857] mean value: 0.584642857142857 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.6 0. 0.6 0.4 1. 0.6 0.6 0.2 0.8 0.8] mean value: 0.5599999999999999 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.5 0.4 0.6 0.7 0.7 0.7 0.5 0.5 0.8 0.6] mean value: 0.5999999999999999 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.5 0.4 0.6 0.7 0.7 0.7 0.5 0.5 0.8 0.6] mean value: 0.6 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.375 0. 0.42857143 0.4 0.625 0.5 0.375 0.16666667 0.66666667 0.5 ] mean value: 0.40369047619047616 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.02 MCC on Training: 0.21 Running classifier: 5 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.08239841 0.08285356 0.08260822 0.0828774 0.08667827 0.08335471 0.0831275 0.0826056 0.0830245 0.08317113] mean value: 0.08326992988586426 key: score_time value: [0.01662946 0.01673603 0.01754904 0.01796627 0.01756358 0.01688862 0.01680303 0.01688099 0.01684093 0.01783872] mean value: 0.017169666290283204 key: test_mcc value: [0. 0.40824829 0.65465367 0.40824829 0.21821789 0.33333333 0.2 0.6 0.5 0.40824829] mean value: 0.3730949765668892 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.44444444 0.72727273 0.83333333 0.66666667 0.66666667 0.33333333 0.6 0.8 0.76923077 0.72727273] mean value: 0.6568220668220668 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.5 0.66666667 0.71428571 0.75 0.57142857 1. 0.6 0.8 0.625 0.66666667] mean value: 0.689404761904762 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.4 0.8 1. 0.6 0.8 0.2 0.6 0.8 1. 0.8] mean value: 0.7 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.5 0.7 0.8 0.7 0.6 0.6 0.6 0.8 0.7 0.7] mean value: 0.67 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.5 0.7 0.8 0.7 0.6 0.6 0.6 0.8 0.7 0.7] mean value: 0.6700000000000002 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.28571429 0.57142857 0.71428571 0.5 0.5 0.2 0.42857143 0.66666667 0.625 0.57142857] mean value: 0.5063095238095239 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.09 MCC on Training: 0.37 Running classifier: 6 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.20457053 0.20452619 0.20014405 0.2010231 0.20946836 0.21474743 0.21326017 0.20426273 0.21156883 0.20764875] mean value: 0.20712201595306395 key: score_time value: [0.00883913 0.00907063 0.00935555 0.00916004 0.01015592 0.00928473 0.00956774 0.00978661 0.00907254 0.00970244] mean value: 0.00939953327178955 key: test_mcc value: [0.40824829 0. 0.81649658 0.21821789 0.40824829 0.21821789 0.81649658 0. 0.6 0.2 ] mean value: 0.36859255232551635 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.72727273 0.44444444 0.88888889 0.5 0.72727273 0.5 0.88888889 0.44444444 0.8 0.6 ] mean value: 0.6521212121212121 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.66666667 0.5 1. 0.66666667 0.66666667 0.66666667 1. 0.5 0.8 0.6 ] mean value: 0.7066666666666666 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.8 0.4 0.8 0.4 0.8 0.4 0.8 0.4 0.8 0.6] mean value: 0.62 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.7 0.5 0.9 0.6 0.7 0.6 0.9 0.5 0.8 0.6] mean value: 0.6799999999999999 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.7 0.5 0.9 0.6 0.7 0.6 0.9 0.5 0.8 0.6] mean value: 0.6799999999999999 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.57142857 0.28571429 0.8 0.33333333 0.57142857 0.33333333 0.8 0.28571429 0.66666667 0.42857143] mean value: 0.5076190476190476 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.01 MCC on Training: 0.37 Running classifier: 7 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.00815296 0.00800729 0.00827289 0.00803828 0.00798154 0.00797057 0.00795388 0.00803947 0.00800681 0.00817013] mean value: 0.008059382438659668 key: score_time value: [0.00828218 0.00829935 0.00831819 0.00821114 0.00828671 0.00822997 0.00828648 0.0082171 0.00837469 0.00823927] mean value: 0.008274507522583009 key: test_mcc value: [ 0.40824829 0.5 0. 0.2 -0.21821789 -0.2 0. 0.2 0.5 0.2 ] mean value: 0.15900304002278706 key: train_mcc value: [0.6350529 0.6350529 0.65025037 0.55766794 0.71269665 0.6 0.60971232 0.67488191 0.57906602 0.64508188] mean value: 0.62994628697647 key: test_fscore value: [0.66666667 0.76923077 0.44444444 0.6 0.25 0.4 0.44444444 0.6 0.76923077 0.6 ] mean value: 0.5544017094017094 key: train_fscore value: [0.79012346 0.79012346 0.80952381 0.69444444 0.85057471 0.8 0.7804878 0.81927711 0.7816092 0.81818182] mean value: 0.793434580708808 key: test_precision value: [0.75 0.625 0.5 0.6 0.33333333 0.4 0.5 0.6 0.625 0.6 ] mean value: 0.5533333333333333 key: train_precision value: [0.88888889 0.88888889 0.87179487 0.92592593 0.88095238 0.8 0.86486486 0.89473684 0.80952381 0.8372093 ] mean value: 0.8662785775270475 key: test_recall value: [0.6 1. 0.4 0.6 0.2 0.4 0.4 0.6 1. 0.6] mean value: 0.58 key: train_recall value: [0.71111111 0.71111111 0.75555556 0.55555556 0.82222222 0.8 0.71111111 0.75555556 0.75555556 0.8 ] mean value: 0.7377777777777779 key: test_accuracy value: [0.7 0.7 0.5 0.6 0.4 0.4 0.5 0.6 0.7 0.6] mean value: 0.5700000000000001 key: train_accuracy value: [0.81111111 0.81111111 0.82222222 0.75555556 0.85555556 0.8 0.8 0.83333333 0.78888889 0.82222222] mean value: 0.8099999999999999 key: test_roc_auc value: [0.7 0.7 0.5 0.6 0.4 0.4 0.5 0.6 0.7 0.6] mean value: 0.5700000000000001 key: train_roc_auc value: [0.81111111 0.81111111 0.82222222 0.75555556 0.85555556 0.8 0.8 0.83333333 0.78888889 0.82222222] mean value: 0.8099999999999999 key: test_jcc value: [0.5 0.625 0.28571429 0.42857143 0.14285714 0.25 0.28571429 0.42857143 0.625 0.42857143] mean value: 0.39999999999999997 key: train_jcc value: [0.65306122 0.65306122 0.68 0.53191489 0.74 0.66666667 0.64 0.69387755 0.64150943 0.69230769] mean value: 0.6592398686553644 MCC on Blind test: 0.17 MCC on Training: 0.16 Running classifier: 8 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.01251221 0.01551986 0.01569295 0.01658797 0.0161221 0.01664901 0.01636219 0.01603198 0.0162499 0.01610589] mean value: 0.015783405303955077 key: score_time value: [0.01131439 0.01127434 0.01186466 0.01187873 0.01191068 0.01199865 0.0120244 0.01186204 0.01183534 0.01188326] mean value: 0.011784648895263672 key: test_mcc value: [ 0.21821789 0.40824829 0. -0.21821789 0.2 0. 0.21821789 0.2 0.6 0.6 ] mean value: 0.2226466180699856 key: train_mcc value: [0.97801929 0.97801929 0.95555556 1. 0.97801929 1. 0.97801929 0.97801929 0.97801929 0.97801929] mean value: 0.9801690612461116 key: test_fscore value: [0.5 0.66666667 0.44444444 0.25 0.6 0.28571429 0.5 0.6 0.8 0.8 ] mean value: 0.5446825396825397 key: train_fscore value: [0.98876404 0.98901099 0.97777778 1. 0.98876404 1. 0.98901099 0.98876404 0.98876404 0.98876404] mean value: 0.9899619980518859 key: test_precision value: [0.66666667 0.75 0.5 0.33333333 0.6 0.5 0.66666667 0.6 0.8 0.8 ] mean value: 0.6216666666666667 key: train_precision value: [1. 0.97826087 0.97777778 1. 1. 1. 0.97826087 1. 1. 1. ] mean value: 0.9934299516908212 key: test_recall value: [0.4 0.6 0.4 0.2 0.6 0.2 0.4 0.6 0.8 0.8] mean value: 0.5 key: train_recall value: [0.97777778 1. 0.97777778 1. 0.97777778 1. 1. 0.97777778 0.97777778 0.97777778] mean value: 0.9866666666666667 key: test_accuracy value: [0.6 0.7 0.5 0.4 0.6 0.5 0.6 0.6 0.8 0.8] mean value: 0.61 key: train_accuracy value: [0.98888889 0.98888889 0.97777778 1. 0.98888889 1. 0.98888889 0.98888889 0.98888889 0.98888889] mean value: 0.99 key: test_roc_auc value: [0.6 0.7 0.5 0.4 0.6 0.5 0.6 0.6 0.8 0.8] mean value: 0.61 key: train_roc_auc value: [0.98888889 0.98888889 0.97777778 1. 0.98888889 1. 0.98888889 0.98888889 0.98888889 0.98888889] mean value: 0.99 key: test_jcc value: [0.33333333 0.5 0.28571429 0.14285714 0.42857143 0.16666667 0.33333333 0.42857143 0.66666667 0.66666667] mean value: 0.3952380952380952 key: train_jcc value: [0.97777778 0.97826087 0.95652174 1. 0.97777778 1. 0.97826087 0.97777778 0.97777778 0.97777778] mean value: 0.9801932367149758 MCC on Blind test: -0.12 MCC on Training: 0.22 Running classifier: 9 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.0203023 0.00874662 0.00847507 0.00925303 0.00915241 0.00906444 0.00890923 0.00927091 0.00901031 0.00901437] mean value: 0.010119867324829102 key: score_time value: [0.02012444 0.01024818 0.00968695 0.01013827 0.01008129 0.01510668 0.01491332 0.01568341 0.01454926 0.01004553] mean value: 0.013057732582092285 key: test_mcc value: [ 0. 0. 0.40824829 0.65465367 -0.2 -0.2 0.6 0.2 0.21821789 0.40824829] mean value: 0.20893681418716956 key: train_mcc value: [0.53452248 0.53346507 0.53346507 0.53346507 0.53665631 0.6 0.56056066 0.48900965 0.44455422 0.42263985] mean value: 0.518833839022752 key: test_fscore value: [0.44444444 0.44444444 0.66666667 0.75 0.4 0.4 0.8 0.6 0.66666667 0.72727273] mean value: 0.589949494949495 key: train_fscore value: [0.75862069 0.76404494 0.76404494 0.76404494 0.75294118 0.8 0.76190476 0.74157303 0.71910112 0.70454545] mean value: 0.7530821071340021 key: test_precision value: [0.5 0.5 0.75 1. 0.4 0.4 0.8 0.6 0.57142857 0.66666667] mean value: 0.6188095238095238 key: train_precision value: [0.78571429 0.77272727 0.77272727 0.77272727 0.8 0.8 0.82051282 0.75 0.72727273 0.72093023] mean value: 0.7722611884239792 key: test_recall value: [0.4 0.4 0.6 0.6 0.4 0.4 0.8 0.6 0.8 0.8] mean value: 0.58 key: train_recall value: [0.73333333 0.75555556 0.75555556 0.75555556 0.71111111 0.8 0.71111111 0.73333333 0.71111111 0.68888889] mean value: 0.7355555555555556 key: test_accuracy value: [0.5 0.5 0.7 0.8 0.4 0.4 0.8 0.6 0.6 0.7] mean value: 0.6 key: train_accuracy value: [0.76666667 0.76666667 0.76666667 0.76666667 0.76666667 0.8 0.77777778 0.74444444 0.72222222 0.71111111] mean value: 0.758888888888889 key: test_roc_auc value: [0.5 0.5 0.7 0.8 0.4 0.4 0.8 0.6 0.6 0.7] mean value: 0.5999999999999999 key: train_roc_auc value: [0.76666667 0.76666667 0.76666667 0.76666667 0.76666667 0.8 0.77777778 0.74444444 0.72222222 0.71111111] mean value: 0.7588888888888888 key: test_jcc value: [0.28571429 0.28571429 0.5 0.6 0.25 0.25 0.66666667 0.42857143 0.5 0.57142857] mean value: 0.43380952380952376 key: train_jcc value: [0.61111111 0.61818182 0.61818182 0.61818182 0.60377358 0.66666667 0.61538462 0.58928571 0.56140351 0.54385965] mean value: 0.604603030479396 MCC on Blind test: -0.12 MCC on Training: 0.21 Running classifier: 10 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.01509309 0.02012157 0.01951075 0.0448277 0.04525232 0.04513931 0.04226112 0.04169655 0.0434947 0.04307199] mean value: 0.03604691028594971 key: score_time value: [0.01171994 0.01186705 0.01182294 0.02076435 0.02207756 0.02060866 0.02213812 0.01492953 0.02100396 0.02300882] mean value: 0.017994093894958495 key: test_mcc value: [-0.40824829 -0.2 -0.21821789 0.65465367 0.40824829 0.21821789 -0.2 0.65465367 0.6 0.2 ] mean value: 0.17093073414159543 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.36363636 0.4 0.25 0.75 0.72727273 0.5 0.4 0.75 0.8 0.6 ] mean value: 0.554090909090909 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.33333333 0.4 0.33333333 1. 0.66666667 0.66666667 0.4 1. 0.8 0.6 ] mean value: 0.6199999999999999 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.4 0.4 0.2 0.6 0.8 0.4 0.4 0.6 0.8 0.6] mean value: 0.52 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.3 0.4 0.4 0.8 0.7 0.6 0.4 0.8 0.8 0.6] mean value: 0.58 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.3 0.4 0.4 0.8 0.7 0.6 0.4 0.8 0.8 0.6] mean value: 0.58 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.22222222 0.25 0.14285714 0.6 0.57142857 0.33333333 0.25 0.6 0.66666667 0.42857143] mean value: 0.4065079365079365 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.35 MCC on Training: 0.17 Running classifier: 11 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.02768087 0.02642035 0.02995276 0.0287962 0.02224755 0.03002977 0.02824306 0.02838469 0.02710295 0.02928448] mean value: 0.027814269065856934 key: score_time value: [0.01144361 0.01154876 0.01142907 0.01141477 0.0114522 0.01143742 0.01145887 0.01145124 0.01145339 0.01146841] mean value: 0.011455774307250977 key: test_mcc value: [0.65465367 0.2 0. 0.6 0.21821789 0.33333333 0. 0.2 0.5 0.40824829] mean value: 0.3114453184741166 key: train_mcc value: [0.80178373 0.8001976 0.82222222 0.73333333 0.84465303 0.84465303 0.77854709 0.77854709 0.8001976 0.8230355 ] mean value: 0.8027170227558983 key: test_fscore value: [0.83333333 0.6 0.28571429 0.8 0.66666667 0.33333333 0.54545455 0.6 0.76923077 0.72727273] mean value: 0.6161005661005661 key: train_fscore value: [0.89655172 0.9010989 0.91111111 0.86666667 0.92134831 0.92307692 0.88636364 0.88636364 0.8988764 0.90909091] mean value: 0.9000548227010838 key: test_precision value: [0.71428571 0.6 0.5 0.8 0.57142857 1. 0.5 0.6 0.625 0.66666667] mean value: 0.6577380952380953 key: train_precision value: [0.92857143 0.89130435 0.91111111 0.86666667 0.93181818 0.91304348 0.90697674 0.90697674 0.90909091 0.93023256] mean value: 0.9095792169856882 key: test_recall value: [1. 0.6 0.2 0.8 0.8 0.2 0.6 0.6 1. 0.8] mean value: 0.66 key: train_recall value: [0.86666667 0.91111111 0.91111111 0.86666667 0.91111111 0.93333333 0.86666667 0.86666667 0.88888889 0.88888889] mean value: 0.8911111111111112 key: test_accuracy value: [0.8 0.6 0.5 0.8 0.6 0.6 0.5 0.6 0.7 0.7] mean value: 0.64 key: train_accuracy value: [0.9 0.9 0.91111111 0.86666667 0.92222222 0.92222222 0.88888889 0.88888889 0.9 0.91111111] mean value: 0.9011111111111111 key: test_roc_auc value: [0.8 0.6 0.5 0.8 0.6 0.6 0.5 0.6 0.7 0.7] mean value: 0.64 key: train_roc_auc value: [0.9 0.9 0.91111111 0.86666667 0.92222222 0.92222222 0.88888889 0.88888889 0.9 0.91111111] mean value: 0.9011111111111111 key: test_jcc value: [0.71428571 0.42857143 0.16666667 0.66666667 0.5 0.2 0.375 0.42857143 0.625 0.57142857] mean value: 0.46761904761904755 key: train_jcc value: [0.8125 0.82 0.83673469 0.76470588 0.85416667 0.85714286 0.79591837 0.79591837 0.81632653 0.83333333] mean value: 0.8186746698679471 MCC on Blind test: -0.06 MCC on Training: 0.31 Running classifier: 12 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(cv=3, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(cv=3, random_state=42))]) key: fit_time value: [0.50018167 0.35693264 0.40602422 0.38526273 0.35025573 0.337116 0.44677591 0.37838507 0.36566067 0.36326146] mean value: 0.3889856100082397 key: score_time value: [0.01174474 0.01179504 0.01187754 0.01170421 0.01169705 0.01174355 0.01167536 0.01173544 0.01185846 0.01174426] mean value: 0.011757564544677735 key: test_mcc value: [ 0.5 0. -0.2 0.81649658 0.21821789 0. -0.21821789 -0.21821789 0. 0.40824829] mean value: 0.13065269811555966 key: train_mcc value: [1. 1. 1. 1. 0.88910845 0.77854709 1. 1. 1. 1. ] mean value: 0.9667655542224001 key: test_fscore value: [0.76923077 0.54545455 0.4 0.88888889 0.66666667 0.28571429 0.5 0.25 0.54545455 0.72727273] mean value: 0.557868242868243 key: train_fscore value: [1. 1. 1. 1. 0.94382022 0.89130435 1. 1. 1. 1. ] mean value: 0.9835124572545189 key: test_precision value: [0.625 0.5 0.4 1. 0.57142857 0.5 0.42857143 0.33333333 0.5 0.66666667] mean value: 0.5525 key: train_precision value: [1. 1. 1. 1. 0.95454545 0.87234043 1. 1. 1. 1. ] mean value: 0.982688588007737 key: test_recall value: [1. 0.6 0.4 0.8 0.8 0.2 0.6 0.2 0.6 0.8] mean value: 0.6 key: train_recall value: [1. 1. 1. 1. 0.93333333 0.91111111 1. 1. 1. 1. ] mean value: 0.9844444444444445 key: test_accuracy value: [0.7 0.5 0.4 0.9 0.6 0.5 0.4 0.4 0.5 0.7] mean value: 0.56 key: train_accuracy value: [1. 1. 1. 1. 0.94444444 0.88888889 1. 1. 1. 1. ] mean value: 0.9833333333333332 key: test_roc_auc value: [0.7 0.5 0.4 0.9 0.6 0.5 0.4 0.4 0.5 0.7] mean value: 0.56 key: train_roc_auc value: [1. 1. 1. 1. 0.94444444 0.88888889 1. 1. 1. 1. ] mean value: 0.9833333333333334 key: test_jcc value: [0.625 0.375 0.25 0.8 0.5 0.16666667 0.33333333 0.14285714 0.375 0.57142857] mean value: 0.4139285714285714 key: train_jcc value: [1. 1. 1. 1. 0.89361702 0.80392157 1. 1. 1. 1. ] mean value: 0.9697538589904047 MCC on Blind test: -0.06 MCC on Training: 0.13 Running classifier: 13 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [0.46474409 0.47209764 0.44391394 0.45461035 0.55081558 0.42466187 0.42950416 0.45154262 0.50548172 0.54625082] mean value: 0.47436227798461916 key: score_time value: [0.01234436 0.01206684 0.01231027 0.01212621 0.0119741 0.01206946 0.01205015 0.0120585 0.01433659 0.01219559] mean value: 0.012353205680847168 key: test_mcc value: [ 0.65465367 0.40824829 0. 0.65465367 0.21821789 0.33333333 -0.21821789 -0.21821789 0.21821789 0.40824829] mean value: 0.24591372556770139 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.83333333 0.66666667 0.44444444 0.75 0.66666667 0.33333333 0.5 0.25 0.66666667 0.72727273] mean value: 0.5838383838383839 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.71428571 0.75 0.5 1. 0.57142857 1. 0.42857143 0.33333333 0.57142857 0.66666667] mean value: 0.6535714285714286 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 0.6 0.4 0.6 0.8 0.2 0.6 0.2 0.8 0.8] mean value: 0.6 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8 0.7 0.5 0.8 0.6 0.6 0.4 0.4 0.6 0.7] mean value: 0.61 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.8 0.7 0.5 0.8 0.6 0.6 0.4 0.4 0.6 0.7] mean value: 0.6100000000000001 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.71428571 0.5 0.28571429 0.6 0.5 0.2 0.33333333 0.14285714 0.5 0.57142857] mean value: 0.43476190476190474 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.14 MCC on Training: 0.25 Running classifier: 14 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.01158166 0.01142573 0.00850725 0.00843453 0.00831437 0.00838399 0.00816393 0.00817609 0.0081737 0.00817919] mean value: 0.00893404483795166 key: score_time value: [0.01122284 0.0112257 0.00862026 0.00849628 0.00842261 0.00832224 0.00820827 0.00814342 0.00823569 0.00810075] mean value: 0.008899807929992676 key: test_mcc value: [ 0.81649658 0.40824829 -0.33333333 0.65465367 0.21821789 -0.2 0. 0.21821789 0. 0. ] mean value: 0.17825009892382176 key: train_mcc value: [0.48997894 0.53346507 0.51314236 0.48900965 0.35634832 0.53452248 0.68888889 0.44455422 0.44455422 0.44455422] mean value: 0.49390183889751327 key: test_fscore value: [0.90909091 0.66666667 0. 0.75 0.66666667 0.4 0.54545455 0.66666667 0.61538462 0.54545455] mean value: 0.5765384615384613 key: train_fscore value: [0.73563218 0.76404494 0.76595745 0.74725275 0.68817204 0.77419355 0.84444444 0.72527473 0.72527473 0.71910112] mean value: 0.7489347931776779 key: test_precision value: [0.83333333 0.75 0. 1. 0.57142857 0.4 0.5 0.57142857 0.5 0.5 ] mean value: 0.5626190476190477 key: train_precision value: [0.76190476 0.77272727 0.73469388 0.73913043 0.66666667 0.75 0.84444444 0.7173913 0.7173913 0.72727273] mean value: 0.7431622794045155 key: test_recall value: [1. 0.6 0. 0.6 0.8 0.4 0.6 0.8 0.8 0.6] mean value: 0.62 key: train_recall value: [0.71111111 0.75555556 0.8 0.75555556 0.71111111 0.8 0.84444444 0.73333333 0.73333333 0.71111111] mean value: 0.7555555555555555 key: test_accuracy value: [0.9 0.7 0.4 0.8 0.6 0.4 0.5 0.6 0.5 0.5] mean value: 0.5900000000000001 key: train_accuracy value: [0.74444444 0.76666667 0.75555556 0.74444444 0.67777778 0.76666667 0.84444444 0.72222222 0.72222222 0.72222222] mean value: 0.7466666666666667 key: test_roc_auc value: [0.9 0.7 0.4 0.8 0.6 0.4 0.5 0.6 0.5 0.5] mean value: 0.5900000000000001 key: train_roc_auc value: [0.74444444 0.76666667 0.75555556 0.74444444 0.67777778 0.76666667 0.84444444 0.72222222 0.72222222 0.72222222] mean value: 0.7466666666666667 key: test_jcc value: [0.83333333 0.5 0. 0.6 0.5 0.25 0.375 0.5 0.44444444 0.375 ] mean value: 0.4377777777777778 key: train_jcc value: [0.58181818 0.61818182 0.62068966 0.59649123 0.52459016 0.63157895 0.73076923 0.56896552 0.56896552 0.56140351] mean value: 0.6003453768569356 MCC on Blind test: -0.06 MCC on Training: 0.18 Running classifier: 15 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.00858331 0.00921011 0.00953889 0.0098927 0.00909543 0.00917506 0.00912452 0.00894547 0.00877857 0.0082736 ] mean value: 0.009061765670776368 key: score_time value: [0.00878119 0.00925946 0.00881577 0.00898075 0.008986 0.00836587 0.00898123 0.00918841 0.00837564 0.00876045] mean value: 0.008849477767944336 key: test_mcc value: [ 0. 0.33333333 0.21821789 0.5 -0.33333333 -0.33333333 -0.65465367 0.21821789 0.2 0.5 ] mean value: 0.06484487764306741 key: train_mcc value: [0.53495589 0.59439629 0.55167728 0.48107024 0.58834841 0.53931937 0.50251891 0.60616081 0.65996633 0.62017367] mean value: 0.5678587197923618 key: test_fscore value: [0.44444444 0.33333333 0.5 0.57142857 0. 0. 0. 0.5 0.6 0.57142857] mean value: 0.35206349206349213 key: train_fscore value: [0.64705882 0.72972973 0.63636364 0.58461538 0.70422535 0.67605634 0.63768116 0.72222222 0.77333333 0.71428571] mean value: 0.6825571693640567 key: test_precision value: [0.5 1. 0.66666667 1. 0. 0. 0. 0.66666667 0.6 1. ] mean value: 0.5433333333333332 key: train_precision value: [0.95652174 0.93103448 1. 0.95 0.96153846 0.92307692 0.91666667 0.96296296 0.96666667 1. ] mean value: 0.9568467902800736 key: test_recall value: [0.4 0.2 0.4 0.4 0. 0. 0. 0.4 0.6 0.4] mean value: 0.28 key: train_recall value: [0.48888889 0.6 0.46666667 0.42222222 0.55555556 0.53333333 0.48888889 0.57777778 0.64444444 0.55555556] mean value: 0.5333333333333334 key: test_accuracy value: [0.5 0.6 0.6 0.7 0.4 0.4 0.2 0.6 0.6 0.7] mean value: 0.53 key: train_accuracy value: [0.73333333 0.77777778 0.73333333 0.7 0.76666667 0.74444444 0.72222222 0.77777778 0.81111111 0.77777778] mean value: 0.7544444444444445 key: test_roc_auc value: [0.5 0.6 0.6 0.7 0.4 0.4 0.2 0.6 0.6 0.7] mean value: 0.53 key: train_roc_auc value: [0.73333333 0.77777778 0.73333333 0.7 0.76666667 0.74444444 0.72222222 0.77777778 0.81111111 0.77777778] mean value: 0.7544444444444445 key: test_jcc value: [0.28571429 0.2 0.33333333 0.4 0. 0. 0. 0.33333333 0.42857143 0.4 ] mean value: 0.23809523809523808 key: train_jcc value: [0.47826087 0.57446809 0.46666667 0.41304348 0.54347826 0.5106383 0.46808511 0.56521739 0.63043478 0.55555556] mean value: 0.520584849419262 MCC on Blind test: 0.03 MCC on Training: 0.06 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.01032257 0.01416397 0.01312423 0.01463294 0.01318336 0.01315379 0.0138402 0.01388645 0.01510906 0.01296353] mean value: 0.013438010215759277 key: score_time value: [0.00913382 0.01167083 0.011657 0.01171064 0.01167774 0.01170516 0.01167917 0.01162291 0.01168108 0.01161528] mean value: 0.011415362358093262 key: test_mcc value: [ 0.2 0. -0.40824829 0.65465367 0.21821789 0.21821789 -0.21821789 -0.21821789 0.5 0.21821789] mean value: 0.11646232704801066 key: train_mcc value: [0.7521398 0.89442719 0.72577474 0.67202151 0.84465303 0.80985829 0.93356387 0.85485041 0.83553169 0.57055978] mean value: 0.7893380309267457 key: test_fscore value: [0.6 0.54545455 0.22222222 0.83333333 0.66666667 0.5 0.5 0.25 0.76923077 0.66666667] mean value: 0.5553574203574204 key: train_fscore value: [0.85 0.94736842 0.86868687 0.8411215 0.92307692 0.90721649 0.96629213 0.91566265 0.91836735 0.8 ] mean value: 0.8937792335361534 key: test_precision value: [0.6 0.5 0.25 0.71428571 0.57142857 0.66666667 0.42857143 0.33333333 0.625 0.57142857] mean value: 0.5260714285714286 key: train_precision value: [0.97142857 0.9 0.7962963 0.72580645 0.91304348 0.84615385 0.97727273 1. 0.8490566 0.67692308] mean value: 0.8655981051721875 key: test_recall value: [0.6 0.6 0.2 1. 0.8 0.4 0.6 0.2 1. 0.8] mean value: 0.62 key: train_recall value: [0.75555556 1. 0.95555556 1. 0.93333333 0.97777778 0.95555556 0.84444444 1. 0.97777778] mean value: 0.9400000000000001 key: test_accuracy value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") [0.6 0.5 0.3 0.8 0.6 0.6 0.4 0.4 0.7 0.6] mean value: 0.55 key: train_accuracy value: [0.86666667 0.94444444 0.85555556 0.81111111 0.92222222 0.9 0.96666667 0.92222222 0.91111111 0.75555556] mean value: 0.8855555555555554 key: test_roc_auc value: [0.6 0.5 0.3 0.8 0.6 0.6 0.4 0.4 0.7 0.6] mean value: 0.55 key: train_roc_auc value: [0.86666667 0.94444444 0.85555556 0.81111111 0.92222222 0.9 0.96666667 0.92222222 0.91111111 0.75555556] mean value: 0.8855555555555554 key: test_jcc value: [0.42857143 0.375 0.125 0.71428571 0.5 0.33333333 0.33333333 0.14285714 0.625 0.5 ] mean value: 0.40773809523809523 key: train_jcc value: [0.73913043 0.9 0.76785714 0.72580645 0.85714286 0.83018868 0.93478261 0.84444444 0.8490566 0.66666667] mean value: 0.8115075889221144 MCC on Blind test: -0.18 MCC on Training: 0.12 Running classifier: 17 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.01429105 0.01515865 0.01431489 0.0155375 0.01435804 0.01425314 0.01487017 0.0143981 0.01495194 0.01422429] mean value: 0.014635777473449707 key: score_time value: [0.01181602 0.01182008 0.01178503 0.01174283 0.01180434 0.0117619 0.01188517 0.01177788 0.01161957 0.01169705] mean value: 0.011770987510681152 key: test_mcc value: [ 0.40824829 0.2 -0.2 -0.5 -0.21821789 -0.21821789 0.6 0.40824829 -0.33333333 0. ] mean value: 0.014672746712240808 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.66666667 0.6 0.4 0.46153846 0.25 0.25 0.8 0.72727273 0.57142857 0.61538462] mean value: 0.5342291042291041 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.75 0.6 0.4 0.375 0.33333333 0.33333333 0.8 0.66666667 0.44444444 0.5 ] mean value: 0.5202777777777777 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.6 0.6 0.4 0.6 0.2 0.2 0.8 0.8 0.8 0.8] mean value: 0.58 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.7 0.6 0.4 0.3 0.4 0.4 0.8 0.7 0.4 0.5] mean value: 0.52 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.7 0.6 0.4 0.3 0.4 0.4 0.8 0.7 0.4 0.5] mean value: 0.52 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.5 0.42857143 0.25 0.3 0.14285714 0.14285714 0.66666667 0.57142857 0.4 0.44444444] mean value: 0.3846825396825396 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.18 MCC on Training: 0.01 Running classifier: 18 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42))]) key: fit_time value: [0.57349849 0.57810116 0.63389635 0.59307694 0.56906295 0.64011455 0.61140823 0.62068796 0.64830804 0.61616969] mean value: 0.6084324359893799 key: score_time value: [0.15469646 0.12577033 0.16959286 0.12422299 0.12287283 0.21519303 0.16829014 0.14378071 0.15454531 0.12254095] mean value: 0.15015056133270263 key: test_mcc value: [0.40824829 0.40824829 0.6 0.6 0.21821789 0.33333333 0.65465367 0.2 0. 0.40824829] mean value: 0.38309497656688923 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.72727273 0.72727273 0.8 0.8 0.66666667 0.33333333 0.75 0.6 0.61538462 0.72727273] mean value: 0.6747202797202797 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.66666667 0.66666667 0.8 0.8 0.57142857 1. 1. 0.6 0.5 0.66666667] mean value: 0.7271428571428572 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.8 0.8 0.8 0.8 0.8 0.2 0.6 0.6 0.8 0.8] mean value: 0.7 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.7 0.7 0.8 0.8 0.6 0.6 0.8 0.6 0.5 0.7] mean value: 0.6799999999999999 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.7 0.7 0.8 0.8 0.6 0.6 0.8 0.6 0.5 0.7] mean value: 0.6799999999999999 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.57142857 0.57142857 0.66666667 0.66666667 0.5 0.2 0.6 0.42857143 0.44444444 0.57142857] mean value: 0.5220634920634921 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.14 MCC on Training: 0.38 Running classifier: 19 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea...age_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42))]) key: fit_time value: [0.93504786 0.90199041 0.89775658 0.96304202 0.90502763 0.87448931 0.8795743 0.90053964 0.9297235 0.92106366] mean value: 0.9108254909515381 key: score_time value: [0.17630005 0.20873356 0.18034601 0.20168066 0.14403558 0.21845126 0.24636102 0.19380021 0.19271708 0.22084832] mean value: 0.198327374458313 key: test_mcc value: [0.40824829 0.40824829 0.81649658 0.81649658 0. 0. 0.65465367 0.2 0. 0.40824829] mean value: 0.3712391703955018 key: train_mcc value: [0.91111111 0.95555556 0.86666667 0.91201231 0.86666667 0.91111111 0.88910845 0.91111111 0.88910845 0.95650071] mean value: 0.9068952143980311 key: test_fscore value: [0.72727273 0.72727273 0.88888889 0.88888889 0.61538462 0.28571429 0.75 0.6 0.61538462 0.72727273] mean value: 0.6826079476079475 key: train_fscore value: [0.95555556 0.97777778 0.93333333 0.95652174 0.93333333 0.95555556 0.94382022 0.95555556 0.94505495 0.97826087] mean value: 0.9534768889580809 key: test_precision value: [0.66666667 0.66666667 1. 1. 0.5 0.5 1. 0.6 0.5 0.66666667] mean value: 0.7100000000000001 key: train_precision value: [0.95555556 0.97777778 0.93333333 0.93617021 0.93333333 0.95555556 0.95454545 0.95555556 0.93478261 0.95744681] mean value: 0.9494056195628815 key: test_recall value: [0.8 0.8 0.8 0.8 0.8 0.2 0.6 0.6 0.8 0.8] mean value: 0.7 key: train_recall value: [0.95555556 0.97777778 0.93333333 0.97777778 0.93333333 0.95555556 0.93333333 0.95555556 0.95555556 1. ] mean value: 0.9577777777777777 key: test_accuracy value: [0.7 0.7 0.9 0.9 0.5 0.5 0.8 0.6 0.5 0.7] mean value: 0.6799999999999999 key: train_accuracy value: [0.95555556 0.97777778 0.93333333 0.95555556 0.93333333 0.95555556 0.94444444 0.95555556 0.94444444 0.97777778] mean value: 0.9533333333333334 key: test_roc_auc value: [0.7 0.7 0.9 0.9 0.5 0.5 0.8 0.6 0.5 0.7] mean value: 0.6799999999999999 key: train_roc_auc value: [0.95555556 0.97777778 0.93333333 0.95555556 0.93333333 0.95555556 0.94444444 0.95555556 0.94444444 0.97777778] mean value: 0.9533333333333335 key: test_jcc value: [0.57142857 0.57142857 0.8 0.8 0.44444444 0.16666667 0.6 0.42857143 0.44444444 0.57142857] mean value: 0.5398412698412699 key: train_jcc value: [0.91489362 0.95652174 0.875 0.91666667 0.875 0.91489362 0.89361702 0.91489362 0.89583333 0.95744681] mean value: 0.9114766419981499 MCC on Blind test: -0.14 MCC on Training: 0.37 Running classifier: 20 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.01843977 0.03219032 0.03260112 0.03274155 0.03266525 0.01273894 0.01289296 0.03727841 0.02923036 0.01422095] mean value: 0.025499963760375978 key: score_time value: [0.01957297 0.02217293 0.02293491 0.0224328 0.02270865 0.01166058 0.01177311 0.01203871 0.02257824 0.01176357] mean value: 0.017963647842407227 key: test_mcc value: [ 0.40824829 0.2 -0.2 1. 0.21821789 0.5 -0.21821789 0.21821789 0.5 0.40824829] mean value: 0.30347144711637186 key: train_mcc value: [0.93356387 0.93356387 0.95650071 0.93541435 0.95555556 0.97801929 0.95650071 0.95650071 0.95650071 0.93356387] mean value: 0.949568366866244 key: test_fscore value: [0.72727273 0.6 0.4 1. 0.66666667 0.57142857 0.5 0.5 0.76923077 0.72727273] mean value: 0.6461871461871462 key: train_fscore value: [0.96629213 0.96703297 0.97727273 0.96551724 0.97777778 0.98876404 0.97727273 0.97727273 0.97727273 0.96629213] mean value: 0.9740767209887705 key: test_precision value: [0.66666667 0.6 0.4 1. 0.57142857 1. 0.42857143 0.66666667 0.625 0.66666667] mean value: 0.6625 key: train_precision value: [0.97727273 0.95652174 1. 1. 0.97777778 1. 1. 1. 1. 0.97727273] mean value: 0.9888844971453666 key: test_recall value: [0.8 0.6 0.4 1. 0.8 0.4 0.6 0.4 1. 0.8] mean value: 0.6799999999999999 key: train_recall value: [0.95555556 0.97777778 0.95555556 0.93333333 0.97777778 0.97777778 0.95555556 0.95555556 0.95555556 0.95555556] mean value: 0.96 key: test_accuracy value: [0.7 0.6 0.4 1. 0.6 0.7 0.4 0.6 0.7 0.7] mean value: 0.64 key: train_accuracy value: [0.96666667 0.96666667 0.97777778 0.96666667 0.97777778 0.98888889 0.97777778 0.97777778 0.97777778 0.96666667] mean value: 0.9744444444444446 key: test_roc_auc value: [0.7 0.6 0.4 1. 0.6 0.7 0.4 0.6 0.7 0.7] mean value: 0.64 key: train_roc_auc value: [0.96666667 0.96666667 0.97777778 0.96666667 0.97777778 0.98888889 0.97777778 0.97777778 0.97777778 0.96666667] mean value: 0.9744444444444446 key: test_jcc value: [0.57142857 0.42857143 0.25 1. 0.5 0.4 0.33333333 0.33333333 0.625 0.57142857] mean value: 0.5013095238095238 key: train_jcc value: [0.93478261 0.93617021 0.95555556 0.93333333 0.95652174 0.97777778 0.95555556 0.95555556 0.95555556 0.93478261] mean value: 0.9495590502621031 MCC on Blind test: -0.18 MCC on Training: 0.3 Running classifier: 21 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=3))]) key: fit_time value: [0.04324627 0.04166913 0.08191776 0.07206559 0.04570484 0.07559109 0.0696075 0.05180192 0.07870555 0.08181977] mean value: 0.06421294212341308 key: score_time value: [0.01367784 0.01370597 0.0219996 0.01243377 0.01335382 0.01680946 0.01253676 0.0163343 0.02490449 0.02131939] mean value: 0.016707539558410645 key: test_mcc value: [ 0.65465367 0. -0.2 0.6 0.21821789 0.33333333 -0.21821789 0.21821789 0.21821789 0.40824829] mean value: 0.2232671074977158 key: train_mcc value: [0.75724019 1. 0.95650071 0.68957028 0.95555556 0.77854709 0.95650071 0.95650071 0.97801929 0.93356387] mean value: 0.8961998425890215 key: test_fscore value: [0.83333333 0.44444444 0.4 0.8 0.66666667 0.33333333 0.5 0.5 0.66666667 0.72727273] mean value: 0.5871717171717172 key: train_fscore value: [0.87356322 1. 0.97727273 0.84090909 0.97777778 0.89130435 0.97727273 0.97727273 0.98876404 0.96629213] mean value: 0.9470428796497223 key: test_precision value: [0.71428571 0.5 0.4 0.8 0.57142857 1. 0.42857143 0.66666667 0.57142857 0.66666667] mean value: 0.6319047619047619 key: train_precision value: [0.9047619 1. 1. 0.86046512 0.97777778 0.87234043 1. 1. 1. 0.97727273] mean value: 0.9592617951623394 key: test_recall value: [1. 0.4 0.4 0.8 0.8 0.2 0.6 0.4 0.8 0.8] mean value: 0.6199999999999999 key: train_recall value: [0.84444444 1. 0.95555556 0.82222222 0.97777778 0.91111111 0.95555556 0.95555556 0.97777778 0.95555556] mean value: 0.9355555555555556 key: test_accuracy value: [0.8 0.5 0.4 0.8 0.6 0.6 0.4 0.6 0.6 0.7] mean value: 0.6 key: train_accuracy value: [0.87777778 1. 0.97777778 0.84444444 0.97777778 0.88888889 0.97777778 0.97777778 0.98888889 0.96666667] mean value: 0.9477777777777778 key: test_roc_auc value: [0.8 0.5 0.4 0.8 0.6 0.6 0.4 0.6 0.6 0.7] mean value: 0.6000000000000001 key: train_roc_auc value: [0.87777778 1. 0.97777778 0.84444444 0.97777778 0.88888889 0.97777778 0.97777778 0.98888889 0.96666667] mean value: 0.9477777777777778 key: test_jcc value: [0.71428571 0.28571429 0.25 0.66666667 0.5 0.2 0.33333333 0.33333333 0.5 0.57142857] mean value: 0.43547619047619046 key: train_jcc value: [0.7755102 1. 0.95555556 0.7254902 0.95652174 0.80392157 0.95555556 0.95555556 0.97777778 0.93478261] mean value: 0.9040670761058045 MCC on Blind test: -0.11 MCC on Training: 0.22 Running classifier: 22 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.02150774 0.00965953 0.01017761 0.00993299 0.00982261 0.01019192 0.01000667 0.01013494 0.00945234 0.00989366] mean value: 0.01107800006866455 key: score_time value: [0.01676893 0.00915647 0.00889087 0.00895119 0.00947738 0.00962758 0.00932193 0.01024413 0.00964522 0.00969958] mean value: 0.010178327560424805 key: test_mcc value: [ 0.40824829 0.40824829 0.40824829 0.65465367 0.21821789 -0.40824829 0. 0.6 0. 0.40824829] mean value: 0.26976164323355584 key: train_mcc value: [0.75724019 0.77854709 0.75574218 0.8001976 0.80178373 0.80178373 0.75574218 0.75574218 0.64700558 0.77777778] mean value: 0.7631562241786438 key: test_fscore value: [0.72727273 0.66666667 0.66666667 0.75 0.66666667 0.22222222 0.44444444 0.8 0.61538462 0.72727273] mean value: 0.6286596736596736 key: train_fscore value: [0.87356322 0.88636364 0.87912088 0.8988764 0.89655172 0.90322581 0.87640449 0.87640449 0.81395349 0.88888889] mean value: 0.8793353034984273 key: test_precision value: [0.66666667 0.75 0.75 1. 0.57142857 0.25 0.5 0.8 0.5 0.66666667] mean value: 0.6454761904761905 key: train_precision value: [0.9047619 0.90697674 0.86956522 0.90909091 0.92857143 0.875 0.88636364 0.88636364 0.85365854 0.88888889] mean value: 0.8909240902203122 key: test_recall value: [0.8 0.6 0.6 0.6 0.8 0.2 0.4 0.8 0.8 0.8] mean value: 0.6399999999999999 key: train_recall value: [0.84444444 0.86666667 0.88888889 0.88888889 0.86666667 0.93333333 0.86666667 0.86666667 0.77777778 0.88888889] mean value: 0.8688888888888888 key: test_accuracy value: [0.7 0.7 0.7 0.8 0.6 0.3 0.5 0.8 0.5 0.7] mean value: 0.63 key: train_accuracy value: [0.87777778 0.88888889 0.87777778 0.9 0.9 0.9 0.87777778 0.87777778 0.82222222 0.88888889] mean value: 0.881111111111111 key: test_roc_auc value: [0.7 0.7 0.7 0.8 0.6 0.3 0.5 0.8 0.5 0.7] mean value: 0.6300000000000001 key: train_roc_auc value: [0.87777778 0.88888889 0.87777778 0.9 0.9 0.9 0.87777778 0.87777778 0.82222222 0.88888889] mean value: 0.881111111111111 key: test_jcc value: [0.57142857 0.5 0.5 0.6 0.5 0.125 0.28571429 0.66666667 0.44444444 0.57142857] mean value: 0.476468253968254 key: train_jcc value: [0.7755102 0.79591837 0.78431373 0.81632653 0.8125 0.82352941 0.78 0.78 0.68627451 0.8 ] mean value: 0.785437274909964 MCC on Blind test: -0.13 MCC on Training: 0.27 Running classifier: 23 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=12, random_state=42))]) key: fit_time value: /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:463: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_CV['source_data'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:490: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_BT['source_data'] = 'BT' [0.01238751 0.01139379 0.01179481 0.01232409 0.01256204 0.01242614 0.01300454 0.01332331 0.01290011 0.01301885] mean value: 0.012513518333435059 key: score_time value: [0.00848699 0.01067042 0.01080823 0.01141357 0.01142025 0.01143384 0.01140499 0.01754403 0.01162219 0.01136017] mean value: 0.01161646842956543 key: test_mcc value: [ 0.65465367 0.5 -0.21821789 0.65465367 0.21821789 0.33333333 0. 0. 0.5 0.40824829] mean value: 0.3050888965213151 key: train_mcc value: [0.77919372 0.64993368 0.62017367 0.49913659 0.86666667 0.68752387 0.93356387 0.95555556 0.93541435 0.77919372] mean value: 0.7706355705214859 key: test_fscore value: [0.83333333 0.76923077 0.5 0.83333333 0.66666667 0.33333333 0.54545455 0.44444444 0.76923077 0.66666667] mean value: 0.6361693861693862 key: train_fscore value: [0.89108911 0.83495146 0.81818182 0.77192982 0.93333333 0.81012658 0.96629213 0.97777778 0.96551724 0.86075949] mean value: 0.8829958771236042 key: test_precision value: [0.71428571 0.625 0.42857143 0.71428571 0.57142857 1. 0.5 0.5 0.625 0.75 ] mean value: 0.6428571428571429 key: train_precision value: [0.80357143 0.74137931 0.69230769 0.63768116 0.93333333 0.94117647 0.97727273 0.97777778 1. 1. ] mean value: 0.8704499899616313 key: test_recall value: [1. 1. 0.6 1. 0.8 0.2 0.6 0.4 1. 0.6] mean value: 0.72 key: train_recall value: [1. 0.95555556 1. 0.97777778 0.93333333 0.71111111 0.95555556 0.97777778 0.93333333 0.75555556] mean value: 0.9199999999999999 key: test_accuracy value: [0.8 0.7 0.4 0.8 0.6 0.6 0.5 0.5 0.7 0.7] mean value: 0.6300000000000001 key: train_accuracy value: [0.87777778 0.81111111 0.77777778 0.71111111 0.93333333 0.83333333 0.96666667 0.97777778 0.96666667 0.87777778] mean value: 0.8733333333333334 key: test_roc_auc value: [0.8 0.7 0.4 0.8 0.6 0.6 0.5 0.5 0.7 0.7] mean value: 0.6300000000000001 key: train_roc_auc value: [0.87777778 0.81111111 0.77777778 0.71111111 0.93333333 0.83333333 0.96666667 0.97777778 0.96666667 0.87777778] mean value: 0.8733333333333334 key: test_jcc value: [0.71428571 0.625 0.33333333 0.71428571 0.5 0.2 0.375 0.28571429 0.625 0.5 ] mean value: 0.4872619047619048 key: train_jcc value: [0.80357143 0.71666667 0.69230769 0.62857143 0.875 0.68085106 0.93478261 0.95652174 0.93333333 0.75555556] mean value: 0.7977161516661979 MCC on Blind test: -0.15 MCC on Training: 0.31 Running classifier: 24 Model_name: XGBoost Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.08880019 0.0496664 0.05317402 0.0706718 0.04711962 0.04969049 0.05696154 0.08282614 0.04657841 0.05065322] mean value: 0.05961418151855469 key: score_time value: [0.01196432 0.01127291 0.01118708 0.00999546 0.01115322 0.01048899 0.01145101 0.01205683 0.01037192 0.0111692 ] mean value: 0.011111092567443848 key: test_mcc value: [0.40824829 0.5 0.5 0.6 0.21821789 0.40824829 0.65465367 0.5 0.6 0.2 ] mean value: 0.4589368141871696 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.72727273 0.76923077 0.57142857 0.8 0.66666667 0.66666667 0.75 0.57142857 0.8 0.6 ] mean value: 0.6922693972693972 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.66666667 0.625 1. 0.8 0.57142857 0.75 1. 1. 0.8 0.6 ] mean value: 0.7813095238095238 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.8 1. 0.4 0.8 0.8 0.6 0.6 0.4 0.8 0.6] mean value: 0.6799999999999999 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.7 0.7 0.7 0.8 0.6 0.7 0.8 0.7 0.8 0.6] mean value: 0.7099999999999999 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.7 0.7 0.7 0.8 0.6 0.7 0.8 0.7 0.8 0.6] mean value: 0.71 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.57142857 0.625 0.4 0.66666667 0.5 0.5 0.6 0.4 0.66666667 0.42857143] mean value: 0.5358333333333334 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.07 MCC on Training: 0.46 Extracting tts_split_name: 80_20 Total cols in each df: CV df: 8 metaDF: 17 Adding column: Model_name Total cols in bts df: BT_df: 8 First proceeding to rowbind CV and BT dfs: Final output should have: 25 columns Combinig 2 using pd.concat by row ~ rowbind Checking Dims of df to combine: Dim of CV: (24, 8) Dim of BT: (24, 8) 8 Number of Common columns: 8 These are: ['source_data', 'Precision', 'JCC', 'MCC', 'Recall', 'Accuracy', 'F1', 'ROC_AUC'] Concatenating dfs with different resampling methods [WF]: Split type: 80_20 No. of dfs combining: 2 PASS: 2 dfs successfully combined nrows in combined_df_wf: 48 ncols in combined_df_wf: 8 PASS: proceeding to merge metadata with CV and BT dfs Adding column: Model_name ========================================================= SUCCESS: Ran multiple classifiers ======================================================= ============================================================== Running several classification models (n): 24 List of models: ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('Gaussian NB', GaussianNB()) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('K-Nearest Neighbors', KNeighborsClassifier()) ('LDA', LinearDiscriminantAnalysis()) ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(cv=3, random_state=42)) [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... 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[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... 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Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. 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Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... ÿKt”bK…”ŒC”t”R”.”…”R”Kdt”}”(h:KŒ check_input”ˆu‡”ahŒThreadingBackend”“”)”}”(Œ nesting_level”KŒinner_max_num_threads”NubN†”N}”t”R”sbŒargs”)Œkwargs”}”Œ loky_pickler”Œ cloudpickle”u[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Multinomial', MultinomialNB()) ('Naive Bayes', BernoulliNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=12, random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Random Forest', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42)) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=3)) ('SVC', SVC(random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=12, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ================================================================ Running classifier: 1 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.1461091 0.13984179 0.13981676 0.14272285 0.13887763 0.13911867 0.14094186 0.13921309 0.13951349 0.14156342] mean value: 0.14077186584472656 key: score_time value: [0.01545978 0.01523423 0.01533556 0.01581931 0.01568317 0.01499104 0.01566076 0.01484299 0.01514864 0.0149262 ] mean value: 0.015310168266296387 key: test_mcc value: [0.85441771 0.86333169 0.90649828 0.81975606 0.8510645 0.77459667 0.8 0.77459667 0.77459667 0.77459667] mean value: 0.8193454916385887 key: train_mcc value: [0.97246592 0.98904035 0.98898063 0.99450549 0.99452051 0.97825827 0.98353133 0.98353133 0.98901099 0.98353133] mean value: 0.9857376160421282 key: test_fscore value: [0.92307692 0.93023256 0.95454545 0.91304348 0.92682927 0.88888889 0.9 0.88888889 0.88888889 0.88888889] mean value: 0.9103283237871022 key: train_fscore value: [0.98630137 0.99453552 0.99447514 0.99724518 0.99726027 0.98913043 0.99178082 0.99178082 0.99450549 0.99178082] mean value: 0.9928795875187737 key: test_precision value: [0.94736842 0.86956522 0.91304348 0.84 0.9047619 0.8 0.9 0.8 0.8 0.8 ] mean value: 0.8574739021466711 key: train_precision value: [0.98360656 0.98913043 0.99447514 0.99450549 0.99453552 0.97849462 0.98907104 0.98907104 0.99450549 0.98907104] mean value: 0.9896466376827888 key: test_recall value: [0.9 1. 1. 1. 0.95 1. 0.9 1. 1. 1. ] mean value: 0.975 key: train_recall value: [0.98901099 1. 0.99447514 1. 1. 1. 0.99450549 0.99450549 0.99450549 0.99450549] mean value: 0.9961508105154515 key: test_accuracy value: [0.92682927 0.92682927 0.95121951 0.90243902 0.925 0.875 0.9 0.875 0.875 0.875 ] mean value: 0.903231707317073 key: train_accuracy value: [0.9862259 0.99449036 0.99449036 0.99724518 0.99725275 0.98901099 0.99175824 0.99175824 0.99450549 0.99175824] mean value: 0.9928495746677566 key: test_roc_auc value: [0.92619048 0.92857143 0.95 0.9 0.925 0.875 0.9 0.875 0.875 0.875 ] mean value: 0.9029761904761905 key: train_roc_auc value: [0.9862182 0.99447514 0.99449032 0.99725275 0.99725275 0.98901099 0.99175824 0.99175824 0.99450549 0.99175824] mean value: 0.9928480359419585 key: test_jcc value: [0.85714286 0.86956522 0.91304348 0.84 0.86363636 0.8 0.81818182 0.8 0.8 0.8 ] mean value: 0.8361569734613212 key: train_jcc value: [0.97297297 0.98913043 0.98901099 0.99450549 0.99453552 0.97849462 0.98369565 0.98369565 0.98907104 0.98369565] mean value: 0.9858808028826769 MCC on Blind test: 0.21 MCC on Training: 0.82 Running classifier: 2 Model_name: Bagging Classifier Model func: BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3))]) key: fit_time value: [0.18911242 0.21649575 0.22356987 0.2228086 0.20202351 0.21887112 0.22028279 0.20051241 0.19893599 0.23238969] mean value: 0.2125002145767212 key: score_time value: [0.06524205 0.06451893 0.08332753 0.05009246 0.06951499 0.04904819 0.14112973 0.07249975 0.06350684 0.07341194] mean value: 0.07322924137115479 key: test_mcc value: [0.95238095 0.95238095 1. 0.95227002 0.8510645 0.80403025 0.70352647 0.85972695 0.81649658 0.90453403] mean value: 0.879641070746364 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.97560976 0.97560976 1. 0.97674419 0.92682927 0.89473684 0.85714286 0.93023256 0.90909091 0.95238095] mean value: 0.9398377085393832 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.95238095 0.95238095 1. 0.95454545 0.9047619 0.94444444 0.81818182 0.86956522 0.83333333 0.90909091] mean value: 0.9138684986511073 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 0.95 0.85 0.9 1. 1. 1. ] mean value: 0.97 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.97560976 0.97560976 1. 0.97560976 0.925 0.9 0.85 0.925 0.9 0.95 ] mean value: 0.9376829268292683 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.97619048 0.97619048 1. 0.975 0.925 0.9 0.85 0.925 0.9 0.95 ] mean value: 0.9377380952380951 key: [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.9s remaining: 1.8s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.9s remaining: 1.8s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.9s remaining: 1.8s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.9s remaining: 1.8s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.9s remaining: 0.3s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.9s remaining: 1.9s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.9s remaining: 1.9s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.0s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.0s remaining: 0.3s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.0s remaining: 0.3s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.0s remaining: 2.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.0s remaining: 2.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.0s remaining: 2.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.0s remaining: 0.3s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.0s remaining: 2.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.1s remaining: 0.4s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.1s remaining: 0.4s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.1s remaining: 0.4s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.1s remaining: 0.4s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.1s remaining: 0.4s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.1s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.1s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.1s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.1s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.1s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. 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[Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.95238095 0.95238095 1. 0.95454545 0.86363636 0.80952381 0.75 0.86956522 0.83333333 0.90909091] mean value: 0.8894456992283079 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.15 MCC on Training: 0.88 Running classifier: 3 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.04709387 0.02886724 0.02661991 0.02570558 0.02664137 0.02742171 0.02380967 0.03031301 0.02892256 0.02478004] mean value: 0.02901749610900879 key: score_time value: [0.01428676 0.01028705 0.01044488 0.01067209 0.0097332 0.00933313 0.0122745 0.01517701 0.01037049 0.01195431] mean value: 0.011453342437744141 key: test_mcc value: [0.90692382 0.78072006 0.81975606 0.86240942 0.80403025 0.75093926 0.85972695 0.77459667 0.73379939 0.77459667] mean value: 0.8067498550652609 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.95238095 0.88888889 0.91304348 0.93333333 0.9047619 0.87179487 0.93023256 0.88888889 0.86956522 0.88888889] mean value: 0.9041778982729438 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.90909091 0.8 0.84 0.875 0.86363636 0.89473684 0.86956522 0.8 0.76923077 0.8 ] mean value: 0.8421260101454611 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 0.95 0.85 1. 1. 1. 1. ] mean value: 0.9800000000000001 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.95121951 0.87804878 0.90243902 0.92682927 0.9 0.875 0.925 0.875 0.85 0.875 ] mean value: 0.8958536585365854 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.95238095 0.88095238 0.9 0.925 0.9 0.875 0.925 0.875 0.85 0.875 ] mean value: 0.8958333333333333 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.90909091 0.8 0.84 0.875 0.82608696 0.77272727 0.86956522 0.8 0.76923077 0.8 ] mean value: 0.8261701124961995 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.18 MCC on Training: 0.81 Running classifier: 4 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.0104351 0.00999951 0.00994182 0.01043105 0.01084232 0.01057124 0.01070952 0.00999379 0.01053977 0.01038647] mean value: 0.01038506031036377 key: score_time value: [0.0093348 0.00883818 0.00982308 0.00887394 0.01102662 0.01016068 0.00973034 0.00931382 0.0094893 0.00951052] mean value: 0.009610128402709962 key: test_mcc value: [0.8547619 0.86333169 0.86240942 0.698212 0.8510645 0.7 0.8 0.69388867 0.73379939 0.85972695] mean value: 0.7917194519248939 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.92682927 0.93023256 0.93333333 0.85714286 0.92682927 0.85 0.9 0.85106383 0.86956522 0.93023256] mean value: 0.8975228890519164 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.9047619 0.86956522 0.875 0.75 0.9047619 0.85 0.9 0.74074074 0.76923077 0.86956522] mean value: 0.8433625754277928 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.95 1. 1. 1. 0.95 0.85 0.9 1. 1. 1. ] mean value: 0.9650000000000001 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.92682927 0.92682927 0.92682927 0.82926829 0.925 0.85 0.9 0.825 0.85 0.925 ] mean value: 0.8884756097560975 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.92738095 0.92857143 0.925 0.825 0.925 0.85 0.9 0.825 0.85 0.925 ] mean value: 0.8880952380952382 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.86363636 0.86956522 0.875 0.75 0.86363636 0.73913043 0.81818182 0.74074074 0.76923077 0.86956522] mean value: 0.8158686924991272 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.01 MCC on Training: 0.79 Running classifier: 5 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.11570191 0.12841463 0.12630177 0.12494159 0.12550044 0.11769247 0.11313987 0.11512423 0.10742736 0.11384773] mean value: 0.11880919933319092 key: score_time value: [0.02319098 0.02150059 0.01959682 0.01929235 0.01960754 0.01854157 0.01906013 0.0183177 0.01757741 0.01895356] mean value: 0.019563865661621094 key: test_mcc value: [0.95227002 0.90692382 0.85441771 0.86240942 0.95118973 0.80403025 0.8510645 0.95118973 0.77459667 0.95118973] mean value: 0.8859281573990447 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.97435897 0.95238095 0.93023256 0.93333333 0.97435897 0.89473684 0.92307692 0.97560976 0.88888889 0.97560976] mean value: 0.9422586958837968 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.90909091 0.90909091 0.875 1. 0.94444444 0.94736842 0.95238095 0.8 0.95238095] mean value: 0.9289756588440798 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.95 1. 0.95238095 1. 0.95 0.85 0.9 1. 1. 1. ] mean value: 0.9602380952380951 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.97560976 0.95121951 0.92682927 0.92682927 0.975 0.9 0.925 0.975 0.875 0.975 ] mean value: 0.9405487804878048 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.975 0.95238095 0.92619048 0.925 0.975 0.9 0.925 0.975 0.875 0.975 ] mean value: 0.9403571428571429 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.95 0.90909091 0.86956522 0.875 0.95 0.80952381 0.85714286 0.95238095 0.8 0.95238095] mean value: 0.8925084697910783 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.04 MCC on Training: 0.89 Running classifier: 6 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.51630425 0.49844027 0.5165801 0.52544308 0.51084185 0.51812077 0.51816702 0.52494502 0.50459003 0.50933337] mean value: 0.5142765760421752 key: score_time value: [0.01008725 0.0091846 0.00948882 0.01004982 0.00928783 0.01027298 0.01069212 0.00992608 0.00940299 0.00937462] mean value: 0.009776711463928223 key: test_mcc value: [0.95227002 0.86333169 0.95227002 0.95227002 0.80403025 0.85972695 0.8510645 0.81649658 0.73379939 0.81649658] mean value: 0.8601755989891124 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.97435897 0.93023256 0.97674419 0.97674419 0.9047619 0.93023256 0.92307692 0.90909091 0.86956522 0.90909091] mean value: 0.9303898326143016 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.86956522 0.95454545 0.95454545 0.86363636 0.86956522 0.94736842 0.83333333 0.76923077 0.83333333] mean value: 0.889512356445995 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.95 1. 1. 1. 0.95 1. 0.9 1. 1. 1. ] mean value: 0.9800000000000001 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.97560976 0.92682927 0.97560976 0.97560976 0.9 0.925 0.925 0.9 0.85 0.9 ] mean value: 0.9253658536585366 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.975 0.92857143 0.975 0.975 0.9 0.925 0.925 0.9 0.85 0.9 ] mean value: 0.9253571428571428 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.95 0.86956522 0.95454545 0.95454545 0.82608696 0.86956522 0.85714286 0.83333333 0.76923077 0.83333333] mean value: 0.8717348593435549 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.23 MCC on Training: 0.86 Running classifier: 7 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.01238108 0.01209664 0.01128221 0.01057482 0.01131225 0.01106858 0.01032543 0.00950766 0.00940394 0.01105118] mean value: 0.010900378227233887 key: score_time value: [0.01098132 0.01091766 0.01004148 0.00963616 0.0103035 0.0102551 0.00881195 0.0088625 0.0088954 0.00906825] mean value: 0.009777331352233886 key: test_mcc value: [0.53206577 0.13342488 0.58066054 0.61152662 0.45056356 0.55068879 0.4 0.30151134 0.15491933 0.45514956] mean value: 0.41705103932096776 key: train_mcc value: [0.50965333 0.57040929 0.47138684 0.50482024 0.4944826 0.52236453 0.48937309 0.57840048 0.54174247 0.54243912] mean value: 0.5225071976662703 key: test_fscore value: [0.70588235 0.60869565 0.75675676 0.81818182 0.73170732 0.7804878 0.7 0.63157895 0.51428571 0.7027027 ] mean value: 0.6950279066361722 key: train_fscore value: [0.75482094 0.78333333 0.70870871 0.74431818 0.72403561 0.75630252 0.7394958 0.79575597 0.75581395 0.75438596] mean value: 0.7516970974706094 key: test_precision value: [0.85714286 0.53846154 0.875 0.7826087 0.71428571 0.76190476 0.7 0.66666667 0.6 0.76470588] mean value: 0.7260776116466653 key: train_precision value: [0.75690608 0.79213483 0.77631579 0.76608187 0.78709677 0.77142857 0.75428571 0.76923077 0.80246914 0.80625 ] mean value: 0.7782199534568527 key: test_recall value: [0.6 0.7 0.66666667 0.85714286 0.75 0.8 0.7 0.6 0.45 0.65 ] mean value: 0.6773809523809524 key: train_recall value: [0.75274725 0.77472527 0.6519337 0.72375691 0.67032967 0.74175824 0.72527473 0.82417582 0.71428571 0.70879121] mean value: 0.7287778519822719 key: test_accuracy value: [0.75609756 0.56097561 0.7804878 0.80487805 0.725 0.775 0.7 0.65 0.575 0.725 ] mean value: 0.7052439024390244 key: train_accuracy value: [0.75482094 0.78512397 0.73278237 0.75206612 0.74450549 0.76098901 0.74450549 0.78846154 0.76923077 0.76923077] mean value: 0.7601716465352829 key: test_roc_auc value: [0.75238095 0.56428571 0.78333333 0.80357143 0.725 0.775 0.7 0.65 0.575 0.725 ] mean value: 0.7053571428571429 key: train_roc_auc value: [0.75482667 0.78515269 0.73256026 0.75198834 0.74450549 0.76098901 0.74450549 0.78846154 0.76923077 0.76923077] mean value: 0.7601451035152692 key: test_jcc value: [0.54545455 0.4375 0.60869565 0.69230769 0.57692308 0.64 0.53846154 0.46153846 0.34615385 0.54166667] mean value: 0.5388701479679741 key: train_jcc value: [0.60619469 0.64383562 0.54883721 0.59276018 0.56744186 0.60810811 0.58666667 0.66079295 0.60747664 0.6056338 ] mean value: 0.6027747722114305 MCC on Blind test: 0.21 MCC on Training: 0.42 Running classifier: 8 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.09538913 0.13720226 0.14461517 0.11250162 0.10209775 0.11587 0.12229657 0.11812329 0.10260034 0.10293698] mean value: 0.11536331176757812 key: score_time value: [0.02156949 0.02283192 0.02238774 0.02336526 0.0232904 0.02270675 0.02369237 0.02243066 0.02230692 0.02485204] mean value: 0.0229433536529541 key: test_mcc value: [0.8047619 0.76500781 0.7098505 0.7197263 0.80403025 0.77459667 0.70352647 0.90453403 0.71443451 0.81649658] mean value: 0.7716965035278722 key: train_mcc value: [0.95111537 0.96717483 0.96180012 0.95644921 0.96225045 0.96755889 0.96755889 0.96225045 0.96726661 0.96225045] mean value: 0.9625675271015044 key: test_fscore value: [0.9 0.88372093 0.86363636 0.86956522 0.9047619 0.88888889 0.85714286 0.95238095 0.86363636 0.90909091] mean value: 0.88928243871621 key: train_fscore value: [0.97574124 0.98369565 0.98092643 0.97826087 0.98113208 0.98378378 0.98378378 0.98113208 0.98369565 0.98113208] mean value: 0.9813283638305601 key: test_precision value: [0.9 0.82608696 0.82608696 0.8 0.86363636 0.8 0.81818182 0.90909091 0.79166667 0.83333333] mean value: 0.8368083003952569 key: train_precision value: [0.95767196 0.97311828 0.96774194 0.96256684 0.96296296 0.96808511 0.96808511 0.96296296 0.97311828 0.96296296] mean value: 0.9659276398870246 key: test_recall value: [0.9 0.95 0.9047619 0.95238095 0.95 1. 0.9 1. 0.95 1. ] mean value: 0.9507142857142856 key: train_recall value: [0.99450549 0.99450549 0.99447514 0.99447514 1. 1. 1. 1. 0.99450549 1. ] mean value: 0.9972466759759577 key: test_accuracy value: [0.90243902 0.87804878 0.85365854 0.85365854 0.9 0.875 0.85 0.95 0.85 0.9 ] mean value: 0.881280487804878 key: train_accuracy value: [0.97520661 0.98347107 0.98071625 0.97796143 0.98076923 0.98351648 0.98351648 0.98076923 0.98351648 0.98076923] mean value: 0.9810212514757968 key: test_roc_auc value: [0.90238095 0.8797619 0.85238095 0.85119048 0.9 0.875 0.85 0.95 0.85 0.9 ] mean value: 0.8810714285714285 key: train_roc_auc value: [0.9751533 0.98344059 0.98075405 0.9780068 0.98076923 0.98351648 0.98351648 0.98076923 0.98351648 0.98076923] mean value: 0.9810211887559955 key: test_jcc value: [0.81818182 0.79166667 0.76 0.76923077 0.82608696 0.8 0.75 0.90909091 0.76 0.83333333] mean value: 0.8017590453025235 key: train_jcc value: [0.95263158 0.96791444 0.96256684 0.95744681 0.96296296 0.96808511 0.96808511 0.96296296 0.96791444 0.96296296] mean value: 0.9633533211037987 MCC on Blind test: -0.02 MCC on Training: 0.77 Running classifier: 9 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.01397824 0.01094079 0.01067638 0.01075339 0.01054025 0.01014423 0.01036239 0.01023912 0.00980639 0.00971317] mean value: 0.010715436935424805 key: score_time value: [0.05231285 0.01361394 0.01316404 0.0137713 0.01299357 0.01724148 0.01274419 0.01556826 0.01761341 0.01288843] mean value: 0.018191146850585937 key: test_mcc value: [0.46623254 0.52420964 0.51320273 0.51966679 0.69388867 0.56803756 0.73379939 0.3 0.37363236 0.6 ] mean value: 0.5292669672837521 key: train_mcc value: [0.66123988 0.71583867 0.72720612 0.69772323 0.72558924 0.72168784 0.7081503 0.67363231 0.72783394 0.71863797] mean value: 0.7077539494531313 key: test_fscore value: [0.7027027 0.77272727 0.77272727 0.7826087 0.85106383 0.8 0.86956522 0.65 0.72340426 0.8 ] mean value: 0.772479924630711 key: train_fscore value: [0.83969466 0.86445013 0.8688946 0.8556962 0.86868687 0.86666667 0.86075949 0.84293194 0.86956522 0.86458333] mean value: 0.8601929105361684 key: test_precision value: [0.76470588 0.70833333 0.73913043 0.72 0.74074074 0.72 0.76923077 0.65 0.62962963 0.8 ] mean value: 0.7241770790070022 key: train_precision value: [0.78199052 0.80861244 0.8125 0.78971963 0.80373832 0.8125 0.79812207 0.805 0.81339713 0.82178218] mean value: 0.8047362278575759 key: test_recall value: [0.65 0.85 0.80952381 0.85714286 1. 0.9 1. 0.65 0.85 0.8 ] mean value: 0.8366666666666667 key: train_recall value: [0.90659341 0.92857143 0.93370166 0.93370166 0.94505495 0.92857143 0.93406593 0.88461538 0.93406593 0.91208791] mean value: 0.9241029688543501 key: test_accuracy value: [0.73170732 0.75609756 0.75609756 0.75609756 0.825 0.775 0.85 0.65 0.675 0.8 ] mean value: 0.7575 key: train_accuracy value: [0.82644628 0.85399449 0.85950413 0.84297521 0.85714286 0.85714286 0.8489011 0.83516484 0.85989011 0.85714286] mean value: 0.8498304725577454 key: test_roc_auc value: [0.7297619 0.75833333 0.7547619 0.75357143 0.825 0.775 0.85 0.65 0.675 0.8 ] mean value: 0.7571428571428571 key: train_roc_auc value: [0.82622488 0.85378848 0.85970797 0.84322446 0.85714286 0.85714286 0.8489011 0.83516484 0.85989011 0.85714286] mean value: 0.8498330398882885 key: test_jcc value: [0.54166667 0.62962963 0.62962963 0.64285714 0.74074074 0.66666667 0.76923077 0.48148148 0.56666667 0.66666667] mean value: 0.633523606023606 key: train_jcc value: [0.72368421 0.76126126 0.76818182 0.74778761 0.76785714 0.76470588 0.75555556 0.72850679 0.76923077 0.76146789] mean value: 0.7548238927823847 MCC on Blind test: -0.0 MCC on Training: 0.53 Running classifier: 10 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.03670907 0.06285739 0.03408647 0.03473973 0.06625795 0.08771539 0.09090328 0.07453322 0.0723207 0.06895089] mean value: 0.06290740966796875 key: score_time value: [0.02075696 0.01198983 0.01190591 0.01200008 0.03499413 0.02077532 0.0204649 0.01559472 0.02073812 0.02362037] mean value: 0.01928403377532959 key: test_mcc value: [0.66668392 0.62776482 0.80817439 0.53206577 0.75858261 0.51031036 0.56803756 0.54554473 0.51031036 0.71443451] mean value: 0.6241909020874157 key: train_mcc value: [0.88730713 0.86964705 0.90132727 0.86333851 0.87071176 0.85269156 0.88623555 0.87071176 0.8799714 0.89670144] mean value: 0.8778643429965541 key: test_fscore value: [0.8372093 0.81632653 0.90909091 0.79166667 0.88372093 0.77272727 0.8 0.79166667 0.77272727 0.86363636] mean value: 0.8238771914685536 key: train_fscore value: [0.94459103 0.93617021 0.95108696 0.93261456 0.93650794 0.92761394 0.944 0.93650794 0.94086022 0.94906166] mean value: 0.9399014444854302 key: test_precision value: [0.7826087 0.68965517 0.86956522 0.7037037 0.82608696 0.70833333 0.72 0.67857143 0.70833333 0.79166667] mean value: 0.7478524507587476 key: train_precision value: [0.90862944 0.90721649 0.93582888 0.91052632 0.90306122 0.90575916 0.91709845 0.90306122 0.92105263 0.92670157] mean value: 0.9138935388403233 key: test_recall value: [0.9 1. 0.95238095 0.9047619 0.95 0.85 0.9 0.95 0.85 0.95 ] mean value: 0.9207142857142856 key: train_recall value: [0.98351648 0.96703297 0.96685083 0.9558011 0.97252747 0.95054945 0.97252747 0.97252747 0.96153846 0.97252747] mean value: 0.967539918644891 key: test_accuracy value: [0.82926829 0.7804878 0.90243902 0.75609756 0.875 0.75 0.775 0.75 0.75 0.85 ] mean value: 0.8018292682926829 key: train_accuracy value: [0.94214876 0.9338843 0.95041322 0.93112948 0.93406593 0.92582418 0.94230769 0.93406593 0.93956044 0.9478022 ] mean value: 0.938120213120213 key: test_roc_auc value: [0.83095238 0.78571429 0.90119048 0.75238095 0.875 0.75 0.775 0.75 0.75 0.85 ] mean value: 0.8020238095238094 key: train_roc_auc value: [0.94203448 0.93379273 0.95045838 0.93119726 0.93406593 0.92582418 0.94230769 0.93406593 0.93956044 0.9478022 ] mean value: 0.9381109222269443 key: test_jcc value: [0.72 0.68965517 0.83333333 0.65517241 0.79166667 0.62962963 0.66666667 0.65517241 0.62962963 0.76 ] mean value: 0.7030925925925926 key: train_jcc value: [0.895 0.88 0.90673575 0.87373737 0.88059701 0.865 0.89393939 0.88059701 0.88832487 0.90306122] mean value: 0.8866992646409093 MCC on Blind test: -0.13 MCC on Training: 0.62 Running classifier: 11 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.04740596 0.03685713 0.03790402 0.0393908 0.03707886 0.03779149 0.0375905 0.03713703 0.0369091 0.0379746 ] mean value: 0.03860394954681397 key: score_time value: [0.01237321 0.01243043 0.01254916 0.01258755 0.01259065 0.01194668 0.01185274 0.01258898 0.01248479 0.01253581] mean value: 0.012393999099731445 key: test_mcc value: [0.61969655 0.52420964 0.61152662 0.7565654 0.75858261 0.56803756 0.75858261 0.40201513 0.3 0.75093926] mean value: 0.6050155365905946 key: train_mcc value: [0.75796619 0.74113433 0.70252876 0.70854484 0.74729787 0.7363082 0.69781273 0.71445829 0.73666416 0.74230749] mean value: 0.7285022865241486 key: test_fscore value: [0.77777778 0.77272727 0.81818182 0.88372093 0.88372093 0.8 0.88372093 0.68421053 0.65 0.87179487] mean value: 0.8025855057495205 key: train_fscore value: [0.88108108 0.8719346 0.85164835 0.85636856 0.87292818 0.86885246 0.84848485 0.85869565 0.87027027 0.87331536] mean value: 0.8653579371942108 key: test_precision value: [0.875 0.70833333 0.7826087 0.86363636 0.82608696 0.72 0.82608696 0.72222222 0.65 0.89473684] mean value: 0.7868711369992835 key: train_precision value: [0.86702128 0.86486486 0.84699454 0.84042553 0.87777778 0.86413043 0.85082873 0.84946237 0.85638298 0.85714286] mean value: 0.8575031352194443 key: test_recall value: [0.7 0.85 0.85714286 0.9047619 0.95 0.9 0.95 0.65 0.65 0.85 ] mean value: 0.8261904761904763 key: train_recall value: [0.8956044 0.87912088 0.85635359 0.87292818 0.86813187 0.87362637 0.84615385 0.86813187 0.88461538 0.89010989] mean value: 0.8734776273450308 key: test_accuracy value: [0.80487805 0.75609756 0.80487805 0.87804878 0.875 0.775 0.875 0.7 0.65 0.875 ] mean value: 0.7993902439024391 key: train_accuracy value: [0.87878788 0.87052342 0.85123967 0.85399449 0.87362637 0.86813187 0.8489011 0.85714286 0.86813187 0.87087912] mean value: 0.8641358641358641 key: test_roc_auc value: [0.80238095 0.75833333 0.80357143 0.87738095 0.875 0.775 0.875 0.7 0.65 0.875 ] mean value: 0.7991666666666667 key: train_roc_auc value: [0.87874142 0.87049967 0.85125372 0.85404651 0.87362637 0.86813187 0.8489011 0.85714286 0.86813187 0.87087912] mean value: 0.8641354501851739 key: test_jcc value: [0.63636364 0.62962963 0.69230769 0.79166667 0.79166667 0.66666667 0.79166667 0.52 0.48148148 0.77272727] mean value: 0.6774176379176379 key: train_jcc value: [0.78743961 0.77294686 0.74162679 0.74881517 0.7745098 0.76811594 0.73684211 0.75238095 0.77033493 0.77511962] mean value: 0.7628131782614311 MCC on Blind test: 0.07 MCC on Training: 0.61 Running classifier: 12 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(cv=3, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(cv=3, random_state=42))]) key: fit_time value: [0.48332763 0.65480113 0.50680137 0.48995638 0.50293756 0.63136077 0.51194048 0.53426313 0.49532986 0.64564729] mean value: 0.5456365585327149 key: score_time value: [0.0123415 0.01366949 0.01284671 0.01280141 0.01276684 0.01409602 0.01287723 0.01235056 0.01242805 0.01281905] mean value: 0.012899684906005859 key: test_mcc value: [0.6806903 0.70272837 0.85441771 0.73786479 0.7 0.81649658 0.77459667 0.61588176 0.54554473 0.85972695] mean value: 0.7287947856471417 key: train_mcc value: [0.96746777 0.96746777 0.9507774 0.97820016 0.92447345 0.95656391 0.96190152 0.97289468 0.93968811 0.98365012] mean value: 0.9603084902663701 key: test_fscore value: [0.84444444 0.85106383 0.93023256 0.875 0.85 0.90909091 0.88888889 0.81632653 0.79166667 0.93023256] mean value: 0.8686946385769458 key: train_fscore value: [0.98378378 0.98378378 0.97547684 0.98907104 0.96256684 0.97837838 0.98102981 0.98644986 0.97002725 0.99182561] mean value: 0.9802393204186327 key: test_precision value: [0.76 0.74074074 0.90909091 0.77777778 0.85 0.83333333 0.8 0.68965517 0.67857143 0.86956522] mean value: 0.7908734579319288 key: train_precision value: [0.96808511 0.96808511 0.96236559 0.97837838 0.9375 0.96276596 0.96791444 0.97326203 0.96216216 0.98378378] mean value: 0.9664302556523175 key: test_recall value: [0.95 1. 0.95238095 1. 0.85 1. 1. 1. 0.95 1. ] mean value: 0.9702380952380952 key: train_recall value: [1. 1. 0.98895028 1. 0.98901099 0.99450549 0.99450549 1. 0.97802198 1. ] mean value: 0.994499423228705 key: test_accuracy value: [0.82926829 0.82926829 0.92682927 0.85365854 0.85 0.9 0.875 0.775 0.75 0.925 ] mean value: 0.8514024390243902 key: train_accuracy value: [0.98347107 0.98347107 0.97520661 0.98898072 0.96153846 0.97802198 0.98076923 0.98626374 0.96978022 0.99175824] mean value: 0.979926134471589 key: test_roc_auc value: [0.83214286 0.83333333 0.92619048 0.85 0.85 0.9 0.875 0.775 0.75 0.925 ] mean value: 0.8516666666666668 key: train_roc_auc value: [0.98342541 0.98342541 0.97524437 0.98901099 0.96153846 0.97802198 0.98076923 0.98626374 0.96978022 0.99175824] mean value: 0.9799238054762917 key: test_jcc value: [0.73076923 0.74074074 0.86956522 0.77777778 0.73913043 0.83333333 0.8 0.68965517 0.65517241 0.86956522] mean value: 0.7705709538393196 key: train_jcc value: [0.96808511 0.96808511 0.95212766 0.97837838 0.92783505 0.95767196 0.96276596 0.97326203 0.94179894 0.98378378] mean value: 0.9613793975052248 MCC on Blind test: 0.01 MCC on Training: 0.73 Running classifier: 13 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [2.06789303 1.54324436 1.46282101 1.61323142 1.55913401 1.45944095 1.61918354 1.61653566 1.56199241 1.43630314] mean value: 1.593977952003479 key: score_time value: [0.01243734 0.01224589 0.01227236 0.01235437 0.01228261 0.01217699 0.01223445 0.0122416 0.0181334 0.01229358] mean value: 0.012867259979248046 key: test_mcc value: [0.95227002 0.8213423 0.85441771 0.77831178 0.80403025 0.69388867 0.75093926 0.85972695 0.69388867 0.95118973] mean value: 0.8160005337544634 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 0.99452051 0.99452051] mean value: 0.9989041013404567 key: test_fscore value: [0.97435897 0.90909091 0.93023256 0.89361702 0.9047619 0.85106383 0.87804878 0.93023256 0.85106383 0.97560976] mean value: 0.9098080121927286 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 0.99724518 0.99724518] mean value: 0.9994490358126722 key: test_precision value: [1. 0.83333333 0.90909091 0.80769231 0.86363636 0.74074074 0.85714286 0.86956522 0.74074074 0.95238095] mean value: 0.8574323422149508 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.95 1. 0.95238095 1. 0.95 1. 0.9 1. 1. 1. ] mean value: 0.9752380952380951 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 0.99450549 0.99450549] mean value: 0.9989010989010989 key: test_accuracy value: [0.97560976 0.90243902 0.92682927 0.87804878 0.9 0.825 0.875 0.925 0.825 0.975 ] mean value: 0.9007926829268292 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 0.99725275 0.99725275] mean value: 0.9994505494505497 key: test_roc_auc value: [0.975 0.9047619 0.92619048 0.875 0.9 0.825 0.875 0.925 0.825 0.975 ] mean value: 0.900595238095238 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 0.99725275 0.99725275] mean value: 0.9994505494505497 key: test_jcc value: [0.95 0.83333333 0.86956522 0.80769231 0.82608696 0.74074074 0.7826087 0.86956522 0.74074074 0.95238095] mean value: 0.8372714161844597 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 0.99450549 0.99450549] mean value: 0.9989010989010989 MCC on Blind test: 0.07 MCC on Training: 0.82 Running classifier: 14 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.0130558 0.01302075 0.00966406 0.00952673 0.0094564 0.00950456 0.00961018 0.00939107 0.00989032 0.01023173] mean value: 0.010335159301757813 key: score_time value: [0.01172805 0.01053452 0.00977588 0.00861049 0.0088892 0.00848579 0.00881028 0.00900531 0.00866032 0.00898767] mean value: 0.009348750114440918 key: test_mcc value: [0.42916625 0.26904762 0.41766229 0.6133669 0.45056356 0.50251891 0.45056356 0.1 0.5 0.60302269] mean value: 0.4335911772863862 key: train_mcc value: [0.54854684 0.55927264 0.47672128 0.5333958 0.50000755 0.55535597 0.48398427 0.49642754 0.60472431 0.55108339] mean value: 0.5309519601675098 key: test_fscore value: [0.64705882 0.63414634 0.7 0.8 0.73170732 0.76190476 0.73170732 0.55 0.75 0.78947368] mean value: 0.7095998245254457 key: train_fscore value: [0.77094972 0.77900552 0.73389356 0.75504323 0.74931129 0.77310924 0.73595506 0.75789474 0.80540541 0.76571429] mean value: 0.7626282053225836 key: test_precision value: [0.78571429 0.61904762 0.73684211 0.84210526 0.71428571 0.72727273 0.71428571 0.55 0.75 0.83333333] mean value: 0.7272886762360445 key: train_precision value: [0.78409091 0.78333333 0.74431818 0.78915663 0.75138122 0.78857143 0.75287356 0.72727273 0.79255319 0.79761905] mean value: 0.7711170224389018 key: test_recall value: [0.55 0.65 0.66666667 0.76190476 0.75 0.8 0.75 0.55 0.75 0.75 ] mean value: 0.6978571428571428 key: train_recall value: [0.75824176 0.77472527 0.72375691 0.72375691 0.74725275 0.75824176 0.71978022 0.79120879 0.81868132 0.73626374] mean value: 0.75519094165503 key: test_accuracy value: [0.70731707 0.63414634 0.70731707 0.80487805 0.725 0.75 0.725 0.55 0.75 0.8 ] mean value: 0.7153658536585366 key: train_accuracy value: [0.77410468 0.77961433 0.73829201 0.76584022 0.75 0.77747253 0.74175824 0.74725275 0.8021978 0.77472527] mean value: 0.7651257833076015 key: test_roc_auc value: [0.70357143 0.63452381 0.70833333 0.80595238 0.725 0.75 0.725 0.55 0.75 0.8 ] mean value: 0.7152380952380952 key: train_roc_auc value: [0.7741485 0.77962783 0.73825208 0.76572461 0.75 0.77747253 0.74175824 0.74725275 0.8021978 0.77472527] mean value: 0.7651159613866796 key: test_jcc value: [0.47826087 0.46428571 0.53846154 0.66666667 0.57692308 0.61538462 0.57692308 0.37931034 0.6 0.65217391] mean value: 0.554838981608097 key: train_jcc value: [0.62727273 0.63800905 0.57964602 0.60648148 0.59911894 0.63013699 0.58222222 0.61016949 0.67420814 0.62037037] mean value: 0.6167635434174124 MCC on Blind test: 0.09 MCC on Training: 0.43 Running classifier: 15 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.00999928 0.01025128 0.009583 0.0104599 0.01028514 0.00962186 0.01028275 0.01028347 0.01066422 0.01088977] mean value: 0.010232067108154297 key: score_time value: [0.00882363 0.00857949 0.00874019 0.0089407 0.00851154 0.00902224 0.00894237 0.00957704 0.00944948 0.00978088] mean value: 0.009036755561828614 key: test_mcc value: [0.41963703 0.22336723 0.26054943 0.35038478 0.5 0.4 0.55068879 0.27994626 0.26688026 0.58713656] mean value: 0.38385903331349863 key: train_mcc value: [0.5033762 0.55352891 0.4959459 0.50446631 0.48404113 0.52265199 0.52094763 0.5306957 0.51692666 0.50454867] mean value: 0.5137129107000921 key: test_fscore value: [0.66666667 0.52941176 0.5 0.58823529 0.75 0.7 0.76923077 0.51612903 0.54545455 0.72727273] mean value: 0.6292400799706303 key: train_fscore value: [0.7195122 0.72077922 0.67558528 0.7047619 0.67326733 0.7073955 0.71875 0.69966997 0.69508197 0.69871795] mean value: 0.7013521312996732 key: test_precision value: [0.75 0.64285714 0.72727273 0.76923077 0.75 0.7 0.78947368 0.72727273 0.69230769 0.92307692] mean value: 0.7471491666228508 key: train_precision value: [0.80821918 0.88095238 0.8559322 0.82835821 0.84297521 0.85271318 0.83333333 0.87603306 0.86178862 0.83846154] mean value: 0.8478766903818062 key: test_recall value: [0.6 0.45 0.38095238 0.47619048 0.75 0.7 0.75 0.4 0.45 0.6 ] mean value: 0.5557142857142856 key: train_recall value: [0.64835165 0.60989011 0.55801105 0.61325967 0.56043956 0.6043956 0.63186813 0.58241758 0.58241758 0.5989011 ] mean value: 0.5989952036913362 key: test_accuracy value: [0.70731707 0.6097561 0.6097561 0.65853659 0.75 0.7 0.775 0.625 0.625 0.775 ] mean value: 0.6835365853658536 key: train_accuracy value: [0.74655647 0.7630854 0.73278237 0.74380165 0.72802198 0.75 0.75274725 0.75 0.74450549 0.74175824] mean value: 0.7453258862349771 key: test_roc_auc value: [0.7047619 0.60595238 0.61547619 0.66309524 0.75 0.7 0.775 0.625 0.625 0.775 ] mean value: 0.6839285714285716 key: train_roc_auc value: [0.74682776 0.76350859 0.73230223 0.74344302 0.72802198 0.75 0.75274725 0.75 0.74450549 0.74175824] mean value: 0.7453114564993017 key: test_jcc value: [0.5 0.36 0.33333333 0.41666667 0.6 0.53846154 0.625 0.34782609 0.375 0.57142857] mean value: 0.4667716196846632 key: train_jcc value: [0.56190476 0.56345178 0.51010101 0.54411765 0.50746269 0.54726368 0.56097561 0.53807107 0.53266332 0.53694581] mean value: 0.5402957369010288 MCC on Blind test: -0.03 MCC on Training: 0.38 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=12, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.01667285 0.01575446 0.01729321 0.01604271 0.01774812 0.02074766 0.01994109 0.02014613 0.01966166 0.01575065] mean value: 0.017975854873657226 key: score_time value: [0.00842214 0.01150584 0.01149845 0.01151013 0.01187348 0.01148748 0.01177239 0.01159453 0.01153636 0.01147604] mean value: 0.011267685890197754 key: test_mcc value: [0.61152662 0.56190476 0.65871309 0.5819877 0.65081403 0.55629391 0.75858261 0.32897585 0.40824829 0.58713656] mean value: 0.5704183413189632 key: train_mcc value: [0.78521983 0.72225214 0.66175352 0.63615215 0.69742172 0.76492161 0.81719329 0.43755995 0.76467178 0.67419986] mean value: 0.6961345868804963 key: test_fscore value: [0.78947368 0.7804878 0.8372093 0.80769231 0.82051282 0.75675676 0.88372093 0.44444444 0.66666667 0.72727273] mean value: 0.7514237444992438 key: train_fscore value: [0.89373297 0.85302594 0.83684211 0.8271028 0.83625731 0.87058824 0.91099476 0.51012146 0.87887324 0.80981595] mean value: 0.8227354773108434 key: test_precision value: [0.83333333 0.76190476 0.81818182 0.67741935 0.84210526 0.82352941 0.82608696 0.85714286 0.75 0.92307692] mean value: 0.8112780679922743 key: train_precision value: [0.88648649 0.8969697 0.79899497 0.71659919 0.89375 0.93670886 0.87 0.96923077 0.9017341 0.91666667] mean value: 0.8787140749317128 key: test_recall value: [0.75 0.8 0.85714286 1. 0.8 0.7 0.95 0.3 0.6 0.6 ] mean value: 0.7357142857142857 key: train_recall value: [0.9010989 0.81318681 0.87845304 0.97790055 0.78571429 0.81318681 0.95604396 0.34615385 0.85714286 0.72527473] mean value: 0.8054155788962418 key: test_accuracy value: [0.80487805 0.7804878 0.82926829 0.75609756 0.825 0.775 0.875 0.625 0.7 0.775 ] mean value: 0.7745731707317074 key: train_accuracy value: [0.89256198 0.85950413 0.8292011 0.79614325 0.84615385 0.87912088 0.90659341 0.66758242 0.88186813 0.82967033] mean value: 0.8388399479308571 key: test_roc_auc value: [0.80357143 0.78095238 0.82857143 0.75 0.825 0.775 0.875 0.625 0.7 0.775 ] mean value: 0.7738095238095238 key: train_roc_auc value: [0.8925384 0.85963208 0.82933641 0.79664258 0.84615385 0.87912088 0.90659341 0.66758242 0.88186813 0.82967033] mean value: 0.8389138485823568 key: test_jcc value: [0.65217391 0.64 0.72 0.67741935 0.69565217 0.60869565 0.79166667 0.28571429 0.5 0.57142857] mean value: 0.6142750617778667 key: train_jcc value: [0.80788177 0.74371859 0.71945701 0.70517928 0.71859296 0.77083333 0.83653846 0.3423913 0.7839196 0.68041237] mean value: 0.7108924695974739 MCC on Blind test: -0.22 MCC on Training: 0.57 Running classifier: 17 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.02693248 0.02617884 0.02719355 0.02656841 0.03372025 0.03643632 0.03054523 0.03300381 0.03879237 0.05242205] mean value: 0.03317933082580567 key: score_time value: [0.01262045 0.01229978 0.0123117 0.01236153 0.01621985 0.02135301 0.01507926 0.01682782 0.01892257 0.01257586] mean value: 0.015057182312011719 key: test_mcc value: [0.95227002 0.95238095 0.95238095 1. 0.95118973 0.85972695 0.90453403 1. 0.95118973 1. ] mean value: 0.9523672369722789 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.97435897 0.97560976 0.97560976 1. 0.97435897 0.91891892 0.94736842 1. 0.97560976 1. ] mean value: 0.9741834556982182 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.95238095 1. 1. 1. 1. 1. 1. 0.95238095 1. ] mean value: 0.9904761904761905 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.95 1. 0.95238095 1. 0.95 0.85 0.9 1. 1. 1. ] mean value: 0.9602380952380951 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.97560976 0.97560976 0.97560976 1. 0.975 0.925 0.95 1. 0.975 1. ] mean value: 0.9751829268292683 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.975 0.97619048 0.97619048 1. 0.975 0.925 0.95 1. 0.975 1. ] mean value: 0.9752380952380951 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.95 0.95238095 0.95238095 1. 0.95 0.85 0.9 1. 0.95238095 1. ] mean value: 0.9507142857142856 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.0 MCC on Training: 0.95 Running classifier: 18 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42))]) key: fit_time value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( [0.62534666 0.67653608 0.65483046 0.68011189 0.63256979 0.65892625 0.72157907 0.65163183 0.62405586 0.6527369 ] mean value: 0.6578324794769287 key: score_time value: [0.19662356 0.12533975 0.18491435 0.17307115 0.13456178 0.12861109 0.24939847 0.16101146 0.1710701 0.15899181] mean value: 0.1683593511581421 key: test_mcc value: [0.95227002 0.95238095 0.85441771 0.90649828 0.8510645 0.7 0.90453403 0.95118973 0.81649658 0.85972695] mean value: 0.8748578750597975 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.97435897 0.97560976 0.93023256 0.95454545 0.92682927 0.85 0.95238095 0.97560976 0.90909091 0.93023256] mean value: 0.9378890187143163 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.95238095 0.90909091 0.91304348 0.9047619 0.85 0.90909091 0.95238095 0.83333333 0.86956522] mean value: 0.9093647656691136 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.95 1. 0.95238095 1. 0.95 0.85 1. 1. 1. 1. ] mean value: 0.9702380952380952 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.97560976 0.97560976 0.92682927 0.95121951 0.925 0.85 0.95 0.975 0.9 0.925 ] mean value: 0.9354268292682928 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.975 0.97619048 0.92619048 0.95 0.925 0.85 0.95 0.975 0.9 0.925 ] mean value: 0.9352380952380953 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.95 0.95238095 0.86956522 0.91304348 0.86363636 0.73913043 0.90909091 0.95238095 0.83333333 0.86956522] mean value: 0.8852126858648596 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.21 MCC on Training: 0.87 Running classifier: 19 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea...age_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42))]) key: fit_time value: [0.98045897 0.97592783 0.96750426 0.95048618 1.05066323 0.96554995 1.00715208 0.95156932 0.95508099 0.95834279] mean value: 0.9762735605239868 key: score_time value: [0.24791718 0.26159 0.24239039 0.27938032 0.21421719 0.22966146 0.14825749 0.18868351 0.24760461 0.2252667 ] mean value: 0.22849688529968262 key: test_mcc value: [0.81975606 0.80907152 0.85441771 0.7565654 0.8510645 0.85972695 0.75093926 0.8510645 0.65081403 0.75858261] mean value: 0.7962002526882632 key: train_mcc value: [0.9338838 0.9339385 0.96156532 0.94496221 0.9399152 0.94528327 0.93457393 0.93957462 0.93957462 0.95067861] mean value: 0.9423950083972029 key: test_fscore value: [0.88888889 0.9047619 0.93023256 0.88372093 0.92682927 0.93023256 0.87804878 0.92682927 0.82926829 0.88372093] mean value: 0.8982533380151076 key: train_fscore value: [0.96703297 0.96721311 0.98082192 0.97252747 0.9701897 0.97282609 0.96756757 0.96986301 0.96986301 0.97547684] mean value: 0.9713381695178184 key: test_precision value: [1. 0.86363636 0.90909091 0.86363636 0.9047619 0.86956522 0.85714286 0.9047619 0.80952381 0.82608696] mean value: 0.8808206286467156 key: train_precision value: [0.96703297 0.96195652 0.97282609 0.96721311 0.95721925 0.96236559 0.95212766 0.96721311 0.96721311 0.96756757] mean value: 0.9642734989867698 key: test_recall value: [0.8 0.95 0.95238095 0.9047619 0.95 1. 0.9 0.95 0.85 0.95 ] mean value: 0.9207142857142856 key: train_recall value: [0.96703297 0.97252747 0.98895028 0.97790055 0.98351648 0.98351648 0.98351648 0.97252747 0.97252747 0.98351648] mean value: 0.97855321474106 key: test_accuracy value: [0.90243902 0.90243902 0.92682927 0.87804878 0.925 0.925 0.875 0.925 0.825 0.875 ] mean value: 0.8959756097560975 key: train_accuracy value: [0.96694215 0.96694215 0.98071625 0.97245179 0.96978022 0.97252747 0.96703297 0.96978022 0.96978022 0.97527473] mean value: 0.9711228165773619 key: test_roc_auc value: [0.9 0.90357143 0.92619048 0.87738095 0.925 0.925 0.875 0.925 0.825 0.875 ] mean value: 0.8957142857142856 key: train_roc_auc value: [0.9669419 0.96692672 0.98073887 0.97246676 0.96978022 0.97252747 0.96703297 0.96978022 0.96978022 0.97527473] mean value: 0.9711250075890959 key: test_jcc value: [0.8 0.82608696 0.86956522 0.79166667 0.86363636 0.86956522 0.7826087 0.86363636 0.70833333 0.79166667] mean value: 0.8166765480895914 key: train_jcc value: [0.93617021 0.93650794 0.96236559 0.94652406 0.94210526 0.94708995 0.93717277 0.94148936 0.94148936 0.95212766] mean value: 0.9443042172938544 MCC on Blind test: 0.18 MCC on Training: 0.8 Running classifier: 20 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.03295159 0.03658748 0.03622055 0.0365119 0.0365243 0.01463556 0.01467061 0.01465607 0.01933408 0.03752184] mean value: 0.02796139717102051 key: score_time value: [0.02172446 0.0232954 0.02165079 0.02298927 0.02051353 0.01194835 0.01176214 0.01184344 0.02228808 0.02204919] mean value: 0.01900646686553955 key: test_mcc value: [0.65871309 0.52420964 0.7565654 0.66432098 0.80403025 0.56803756 0.65081403 0.35043832 0.4 0.65743826] mean value: 0.6034567533777953 key: train_mcc value: [0.82491677 0.81309819 0.81393516 0.81971714 0.83764676 0.83179497 0.83028467 0.82497319 0.81441709 0.83118986] mean value: 0.8241973806257212 key: test_fscore value: [0.82051282 0.77272727 0.88372093 0.84444444 0.9047619 0.8 0.82926829 0.66666667 0.7 0.8372093 ] mean value: 0.8059311634354176 key: train_fscore value: [0.9144385 0.90810811 0.90860215 0.91152815 0.92063492 0.91777188 0.91644205 0.91397849 0.90909091 0.91733333] mean value: 0.9137928500943051 key: test_precision value: [0.84210526 0.70833333 0.86363636 0.79166667 0.86363636 0.72 0.80952381 0.68421053 0.7 0.7826087 ] mean value: 0.7765721021922395 key: train_precision value: [0.890625 0.89361702 0.88481675 0.88541667 0.8877551 0.88717949 0.8994709 0.89473684 0.88541667 0.89119171] mean value: 0.8900226149177655 key: test_recall value: [0.8 0.85 0.9047619 0.9047619 0.95 0.9 0.85 0.65 0.7 0.9 ] mean value: 0.840952380952381 key: train_recall value: [0.93956044 0.92307692 0.93370166 0.93922652 0.95604396 0.95054945 0.93406593 0.93406593 0.93406593 0.94505495] mean value: 0.9389411693279097 key: test_accuracy value: [0.82926829 0.75609756 0.87804878 0.82926829 0.9 0.775 0.825 0.675 0.7 0.825 ] mean value: 0.7992682926829269 key: train_accuracy value: [0.91184573 0.90633609 0.90633609 0.90909091 0.91758242 0.91483516 0.91483516 0.91208791 0.90659341 0.91483516] mean value: 0.9114378046196228 key: test_roc_auc value: [0.82857143 0.75833333 0.87738095 0.82738095 0.9 0.775 0.825 0.675 0.7 0.825 ] mean value: 0.7991666666666667 key: train_roc_auc value: [0.91176917 0.90628984 0.90641127 0.9091737 0.91758242 0.91483516 0.91483516 0.91208791 0.90659341 0.91483516] mean value: 0.9114413211098293 key: test_jcc value: [0.69565217 0.62962963 0.79166667 0.73076923 0.82608696 0.66666667 0.70833333 0.5 0.53846154 0.72 ] mean value: 0.6807266195961847 key: train_jcc value: [0.84236453 0.83168317 0.83251232 0.83743842 0.85294118 0.84803922 0.84577114 0.84158416 0.83333333 0.84729064] mean value: 0.8412958107831525 MCC on Blind test: -0.02 MCC on Training: 0.6 Running classifier: 21 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=3))]) key: fit_time value: [0.13692307 0.15678477 0.12627888 0.19739866 0.15301466 0.10767031 0.07531548 0.05776 0.06532454 0.1181736 ] mean value: 0.11946439743041992 key: score_time value: [0.01513076 0.02415013 0.02311826 0.02434158 0.02356672 0.01502752 0.01210809 0.01196909 0.0218327 0.02742195] mean value: 0.019866681098937987 key: test_mcc value: [0.65871309 0.52420964 0.7565654 0.57570364 0.80403025 0.60302269 0.65081403 0.48038446 0.61237244 0.65743826] mean value: 0.6323253898163907 key: train_mcc value: [0.82491677 0.81309819 0.81393516 0.85696504 0.87071176 0.86421727 0.83028467 0.86421727 0.88569897 0.83118986] mean value: 0.845523497765245 key: test_fscore value: [0.82051282 0.77272727 0.88372093 0.80851064 0.9047619 0.80952381 0.82926829 0.76595745 0.81818182 0.8372093 ] mean value: 0.8250374236055075 key: train_fscore value: [0.9144385 0.90810811 0.90860215 0.92896175 0.93650794 0.93333333 0.91644205 0.93333333 0.94369973 0.91733333] mean value: 0.9240760226882362 key: test_precision value: [0.84210526 0.70833333 0.86363636 0.73076923 0.86363636 0.77272727 0.80952381 0.66666667 0.75 0.7826087 ] mean value: 0.779000699910311 key: train_precision value: [0.890625 0.89361702 0.88481675 0.91891892 0.90306122 0.90673575 0.8994709 0.90673575 0.92146597 0.89119171] mean value: 0.9016638999104531 key: test_recall value: [0.8 0.85 0.9047619 0.9047619 0.95 0.85 0.85 0.9 0.9 0.9 ] mean value: 0.880952380952381 key: train_recall value: [0.93956044 0.92307692 0.93370166 0.93922652 0.97252747 0.96153846 0.93406593 0.96153846 0.96703297 0.94505495] mean value: 0.9477323781191185 key: test_accuracy value: [0.82926829 0.75609756 0.87804878 0.7804878 0.9 0.8 0.825 0.725 0.8 0.825 ] mean value: 0.811890243902439 key: train_accuracy value: [0.91184573 0.90633609 0.90633609 0.92837466 0.93406593 0.93131868 0.91483516 0.93131868 0.94230769 0.91483516] mean value: 0.9221573880664788 key: test_roc_auc value: [0.82857143 0.75833333 0.87738095 0.77738095 0.9 0.8 0.825 0.725 0.8 0.825 ] mean value: 0.8116666666666668 key: train_roc_auc value: [0.91176917 0.90628984 0.90641127 0.92840447 0.93406593 0.93131868 0.91483516 0.93131868 0.94230769 0.91483516] mean value: 0.9221556068241149 key: test_jcc value: [0.69565217 0.62962963 0.79166667 0.67857143 0.82608696 0.68 0.70833333 0.62068966 0.69230769 0.72 ] mean value: 0.7042937536115947 key: train_jcc value: [0.84236453 0.83168317 0.83251232 0.86734694 0.88059701 0.875 0.84577114 0.875 0.89340102 0.84729064] mean value: 0.8590966769209476 MCC on Blind test: -0.16 MCC on Training: 0.63 Running classifier: 22 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.03620052 0.01889014 0.01866221 0.01888084 0.01575899 0.0158844 0.01582122 0.01855373 0.0164547 0.01616716] mean value: 0.019127392768859865 key: score_time value: [0.01288462 0.01150393 0.01217604 0.0121429 0.01065326 0.01141548 0.01156616 0.01178956 0.01098466 0.01264572] mean value: 0.011776232719421386 key: test_mcc value: [0.698212 0.41766229 0.7098505 0.7098505 0.71443451 0.55629391 0.81649658 0.55629391 0.55068879 0.65081403] mean value: 0.6380597028398078 key: train_mcc value: [0.7088191 0.71354847 0.71909158 0.73032312 0.69781273 0.74185903 0.70346662 0.69806568 0.74185903 0.69781273] mean value: 0.7152658096057779 key: test_fscore value: [0.78787879 0.71428571 0.86363636 0.86363636 0.86363636 0.79069767 0.90909091 0.75675676 0.76923077 0.82051282] mean value: 0.8139362523083452 key: train_fscore value: [0.85790885 0.85635359 0.85793872 0.86648501 0.84848485 0.8719346 0.85 0.84679666 0.8719346 0.84931507] mean value: 0.8577151954801018 key: test_precision value: [1. 0.68181818 0.82608696 0.82608696 0.79166667 0.73913043 0.83333333 0.82352941 0.78947368 0.84210526] mean value: 0.8153230888777395 key: train_precision value: [0.83769634 0.86111111 0.86516854 0.85483871 0.85082873 0.86486486 0.85955056 0.85875706 0.86486486 0.84699454] mean value: 0.8564675313668177 key: test_recall value: [0.65 0.75 0.9047619 0.9047619 0.95 0.85 1. 0.7 0.75 0.8 ] mean value: 0.825952380952381 key: train_recall value: [0.87912088 0.85164835 0.85082873 0.87845304 0.84615385 0.87912088 0.84065934 0.83516484 0.87912088 0.85164835] mean value: 0.8591919130593165 key: test_accuracy value: [0.82926829 0.70731707 0.85365854 0.85365854 0.85 0.775 0.9 0.775 0.775 0.825 ] mean value: 0.8143902439024391 key: train_accuracy value: [0.85399449 0.85674931 0.85950413 0.86501377 0.8489011 0.87087912 0.85164835 0.8489011 0.87087912 0.8489011 ] mean value: 0.8575371598098871 key: test_roc_auc value: [0.825 0.70833333 0.85238095 0.85238095 0.85 0.775 0.9 0.775 0.775 0.825 ] mean value: 0.8138095238095238 key: train_roc_auc value: [0.85392508 0.8567634 0.8594803 0.8650507 0.8489011 0.87087912 0.85164835 0.8489011 0.87087912 0.8489011 ] mean value: 0.857532936676583 key: test_jcc value: [0.65 0.55555556 0.76 0.76 0.76 0.65384615 0.83333333 0.60869565 0.625 0.69565217] mean value: 0.6902082868821999 key: train_jcc value: [0.75117371 0.74879227 0.75121951 0.76442308 0.73684211 0.77294686 0.73913043 0.73429952 0.77294686 0.73809524] mean value: 0.7509869583425768 MCC on Blind test: 0.05 MCC on Training: 0.64 Running classifier: 23 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.01545262 0.01935458 0.02003527 0.01932406 0.0225668 0.0179913 0.03679204 0.02011847 0.0215075 0.0220449 ] mean value: 0.021518754959106445 key: score_time value: [0.00983262 0.01141214 0.01169634 0.01210856 0.01224589 0.01205921 0.02443814 0.01264215 0.01199222 0.01200104] mean value: 0.013042831420898437 key: test_mcc value: [0.44064783 0.22099785 0.73786479 0.7633652 0.80403025 0.56803756 0.65081403 0.39192476 0.43643578 0.71443451] mean value: 0.5728552541482784 key: train_mcc value: [0.53742066 0.3961226 0.63193205 0.82369012 0.85979853 0.76374779 0.78002539 0.64733212 0.78621412 0.82784166] mean value: 0.7054125048613554 key: test_fscore value: [0.74509804 0.6779661 0.875 0.88888889 0.9047619 0.8 0.82051282 0.73469388 0.75 0.86363636] mean value: 0.80605579962616 key: train_fscore value: [0.78448276 0.73387097 0.82272727 0.91160221 0.93059126 0.88219178 0.86850153 0.83294118 0.89620253 0.91560102] mean value: 0.857871250968272 key: test_precision value: [0.61290323 0.51282051 0.77777778 0.83333333 0.86363636 0.72 0.84210526 0.62068966 0.64285714 0.79166667] mean value: 0.7217789941228558 key: train_precision value: [0.64539007 0.57961783 0.6988417 0.91160221 0.87439614 0.87978142 0.97931034 0.72839506 0.83098592 0.85645933] mean value: 0.7984780022326549 key: test_recall value: [0.95 1. 1. 0.95238095 0.95 0.9 0.8 0.9 0.9 0.95 ] mean value: 0.9302380952380952 key: train_recall value: [1. 1. 1. 0.91160221 0.99450549 0.88461538 0.78021978 0.97252747 0.97252747 0.98351648] mean value: 0.9499514297856839 key: test_accuracy value: [0.68292683 0.53658537 0.85365854 0.87804878 0.9 0.775 0.825 0.675 0.7 0.85 ] mean value: 0.7676219512195122 key: train_accuracy value: [0.72451791 0.63636364 0.78512397 0.91184573 0.92582418 0.88186813 0.88186813 0.80494505 0.88736264 0.90934066] mean value: 0.8349060030878211 key: test_roc_auc value: [0.68928571 0.54761905 0.85 0.87619048 0.9 0.775 0.825 0.675 0.7 0.85 ] mean value: 0.7688095238095237 key: train_roc_auc value: [0.72375691 0.63535912 0.78571429 0.91184506 0.92582418 0.88186813 0.88186813 0.80494505 0.88736264 0.90934066] mean value: 0.8347884160038858 key: test_jcc value: [0.59375 0.51282051 0.77777778 0.8 0.82608696 0.66666667 0.69565217 0.58064516 0.6 0.76 ] mean value: 0.6813399248990062 key: train_jcc value: [0.64539007 0.57961783 0.6988417 0.83756345 0.87019231 0.78921569 0.76756757 0.71370968 0.81192661 0.84433962] mean value: 0.7558364523035076 MCC on Blind test: 0.06 MCC on Training: 0.57 Running classifier: 24 Model_name: XGBoost Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.12174511 0.10939527 0.07868552 0.07986951 0.07703543 0.08636642 0.08102536 0.11617064 0.08776808 0.09007692] mean value: 0.09281382560729981 key: score_time value: [0.01098537 0.01114202 0.0112021 0.01100612 0.01082492 0.01102781 0.01236582 0.01177049 0.01089597 0.01122117] mean value: 0.01124417781829834 key: test_mcc value: [0.95227002 0.95238095 0.95227002 0.90649828 0.9 0.85972695 0.8510645 0.95118973 0.77459667 0.81649658] mean value: 0.8916493693430819 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.97435897 0.97560976 0.97674419 0.95454545 0.95 0.93023256 0.92307692 0.97560976 0.88888889 0.90909091] mean value: 0.9458157406342318 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.95238095 0.95454545 0.91304348 0.95 0.86956522 0.94736842 0.95238095 0.8 0.83333333] /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:463: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_CV['source_data'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:490: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_BT['source_data'] = 'BT' mean value: 0.91726178093455 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.95 1. 1. 1. 0.95 1. 0.9 1. 1. 1. ] mean value: 0.9800000000000001 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.97560976 0.97560976 0.97560976 0.95121951 0.95 0.925 0.925 0.975 0.875 0.9 ] mean value: 0.9428048780487804 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.975 0.97619048 0.975 0.95 0.95 0.925 0.925 0.975 0.875 0.9 ] mean value: 0.9426190476190476 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.95 0.95238095 0.95454545 0.91304348 0.9047619 0.86956522 0.85714286 0.95238095 0.8 0.83333333] mean value: 0.8987154150197629 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.3 MCC on Training: 0.89 Extracting tts_split_name: 80_20 Total cols in each df: CV df: 8 metaDF: 17 Adding column: Model_name Total cols in bts df: BT_df: 8 First proceeding to rowbind CV and BT dfs: Final output should have: 25 columns Combinig 2 using pd.concat by row ~ rowbind Checking Dims of df to combine: Dim of CV: (24, 8) Dim of BT: (24, 8) 8 Number of Common columns: 8 These are: ['source_data', 'Precision', 'JCC', 'MCC', 'Recall', 'Accuracy', 'F1', 'ROC_AUC'] Concatenating dfs with different resampling methods [WF]: Split type: 80_20 No. of dfs combining: 2 PASS: 2 dfs successfully combined nrows in combined_df_wf: 48 ncols in combined_df_wf: 8 PASS: proceeding to merge metadata with CV and BT dfs Adding column: Model_name ========================================================= SUCCESS: Ran multiple classifiers ======================================================= Input params: Dim of input df: (858, 175) Data type to split: complete Split type: 80_20 target colname: dst_mode oversampling enabled PASS: x_features has no target variable and no dst column Dropped cols: 2 These were: dst_mode and dst No. of cols in input df: 175 No.of cols dropped: 2 No. of columns for x_features: 173 ------------------------------------------------------------- Successfully generated training and test data: Data used: complete Split type: 80_20 Total no. of input features: 173 --------No. of numerical features: 167 --------No. of categorical features: 6 =========================== Resampling: NONE Baseline =========================== Total data size: 858 Train data size: (686, 173) y_train numbers: Counter({0: 584, 1: 102}) Test data size: (172, 173) y_test_numbers: Counter({0: 147, 1: 25}) y_train ratio: 5.7254901960784315 y_test ratio: 5.88 ------------------------------------------------------------- Simple Random OverSampling Counter({0: 584, 1: 584}) (1168, 173) Simple Random UnderSampling Counter({0: 102, 1: 102}) (204, 173) Simple Combined Over and UnderSampling Counter({0: 584, 1: 584}) (1168, 173) SMOTE_NC OverSampling Counter({0: 584, 1: 584}) (1168, 173) Generated Resampled data as below: ================================= Resampling: Random oversampling ================================ Train data size: (1168, 173) y_train numbers: 1168 y_train ratio: 1.0 y_test ratio: 5.88 ================================ Resampling: Random underampling ================================ Train data size: (204, 173) y_train numbers: 204 y_train ratio: 1.0 y_test ratio: 5.88 ================================ Resampling:Combined (over+under) ================================ Train data size: (1168, 173) y_train numbers: 1168 y_train ratio: 1.0 y_test ratio: 5.88 ============================== Resampling: Smote NC ============================== Train data size: (1168, 173) y_train numbers: 1168 y_train ratio: 1.0 y_test ratio: 5.88 ------------------------------------------------------------- ============================================================== Running several classification models (n): 24 List of models: ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('Gaussian NB', GaussianNB()) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('K-Nearest Neighbors', KNeighborsClassifier()) ('LDA', LinearDiscriminantAnalysis()) ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(cv=3, random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Multinomial', MultinomialNB()) ('Naive Bayes', BernoulliNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=12, random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Random Forest', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42)) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=3)) ('SVC', SVC(random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=12, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ================================================================ Running classifier: 1 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.20547581 0.21017718 0.21383476 0.21787405 0.21577811 0.20936131 0.20587587 0.21359611 0.21746111 0.21128297] mean value: 0.21207172870635987 key: score_time value: [0.01549268 0.01631856 0.01650929 0.01709294 0.01711345 0.01657557 0.01643658 0.01670527 0.01611972 0.01719642] mean value: 0.016556048393249513 key: test_mcc value: [ 0.16516678 0.25021503 0.15810128 0.16516678 -0.01520319 0.03098437 0.456405 0.26926311 0.4059041 0.23501039] mean value: 0.21210136545722516 key: train_mcc value: [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.6s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... 0Ï5"ÿq§#X­KKKK¡àÛ—C"@Í3Ê®U[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.6s Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.6s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.6s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... 2@@ @@$@C@@Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 2.0s remaining: 4.0s Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 2.2s remaining: 0.7s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 2.2s remaining: 4.5s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 2.2s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 2.2s remaining: 4.5s Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 2.3s remaining: 4.6s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 2.3s remaining: 4.7s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 2.4s remaining: 0.8s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 2.4s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 2.4s remaining: 0.8s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 2.4s remaining: 4.8s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 2.4s remaining: 0.8s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 2.4s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 2.4s remaining: 0.8s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 2.5s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 2.5s remaining: 0.8s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 2.5s remaining: 5.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 2.5s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 2.5s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 2.6s remaining: 0.9s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 2.6s remaining: 5.2s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 2.6s remaining: 0.9s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 2.6s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 2.6s remaining: 5.3s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 2.6s remaining: 5.3s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 2.7s remaining: 0.9s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 2.7s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 2.7s remaining: 0.9s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 2.7s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 2.7s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.6s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [0.6678476 0.61101373 0.7231294 0.69426757 0.64912932 0.6738356 0.64859165 0.66853273 0.64034234 0.6925506 ] mean value: 0.6669240548319906 key: test_fscore value: [0.25 0.28571429 0.28571429 0.25 0.11111111 0.125 0.5 0.35294118 0.47058824 0.33333333] mean value: 0.2964402427637721 key: train_fscore value: [0.68456376 0.63013699 0.74358974 0.72151899 0.67114094 0.69736842 0.67948718 0.68874172 0.67096774 0.71428571] mean value: 0.6901801193834777 key: test_precision value: [0.33333333 0.5 0.27272727 0.33333333 0.14285714 0.2 0.66666667 0.42857143 0.57142857 0.375 ] mean value: 0.3823917748917748 key: train_precision value: [0.89473684 0.85185185 0.90625 0.86363636 0.86206897 0.86885246 0.828125 0.88135593 0.82539683 0.88709677] mean value: 0.8669371013920877 key: test_recall value: [0.2 0.2 0.3 0.2 0.09090909 0.09090909 0.4 0.3 0.4 0.3 ] mean value: 0.24818181818181814 key: train_recall value: [0.55434783 0.5 0.63043478 0.61956522 0.54945055 0.58241758 0.57608696 0.56521739 0.56521739 0.59782609] mean value: 0.5740563784042044 key: test_accuracy value: [0.82608696 0.85507246 0.7826087 0.82608696 0.76811594 0.79710145 0.88235294 0.83823529 0.86764706 0.82352941] mean value: 0.8266837169650468 key: train_accuracy value: [0.92382496 0.91247974 0.93517018 0.9286872 0.92058347 0.92544571 0.91909385 0.92394822 0.91747573 0.92880259] mean value: 0.9235511636323583 key: test_roc_auc value: [0.56610169 0.58305085 0.58220339 0.56610169 0.49373041 0.51097179 0.68275862 0.61551724 0.67413793 0.60689655] mean value: 0.5881470166303597 key: train_roc_auc value: [0.77145963 0.74238095 0.80950311 0.80121118 0.76712071 0.78360423 0.7775872 0.7759547 0.77215242 0.79225905] mean value: 0.7793233186607298 key: test_jcc value: [0.14285714 0.16666667 0.16666667 0.14285714 0.05882353 0.06666667 0.33333333 0.21428571 0.30769231 0.2 ] mean value: 0.17998491704374056 key: train_jcc value: [0.52040816 0.46 0.59183673 0.56435644 0.50505051 0.53535354 0.51456311 0.52525253 0.50485437 0.55555556] mean value: 0.5277230930543024 MCC on Blind test: 0.25 MCC on Training: 0.21 Running classifier: 2 Model_name: Bagging Classifier Model func: BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3))]) key: fit_time value: [0.42077565 0.38901997 0.40518785 0.38078928 0.4277606 0.38090467 0.37642694 0.38888192 0.43204284 0.3920064 ] mean value: 0.3993796110153198 key: score_time value: [0.04211545 0.03966546 0.05405927 0.04458904 0.06327891 0.05062151 0.06579518 0.07953358 0.07265878 0.07431674] mean value: 0.05866339206695557 key: test_mcc value: [0.42638684 0.36132554 0.41966582 0.42638684 0.03098437 0.16074413 0.60207973 0.36028937 0.41923451 0.31519155] mean value: 0.3522288697308752 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.42857143 0.4 0.33333333 0.42857143 0.125 0.15384615 0.57142857 0.4 0.33333333 0.30769231] mean value: 0.3481776556776557 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.75 0.6 1. 0.75 0.2 0.5 1. 0.6 1. 0.66666667] mean value: 0.7066666666666668 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.3 0.3 0.2 0.3 0.09090909 0.09090909 0.4 0.3 0.2 0.2 ] mean value: 0.23818181818181822 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.88405797 0.86956522 0.88405797 0.88405797 0.79710145 0.84057971 0.91176471 0.86764706 0.88235294 0.86764706] mean value: 0.8688832054560954 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.64152542 0.63305085 0.6 0.64152542 0.51097179 0.53683386 0.7 0.63275862 0.6 0.59137931] mean value: 0.6088045268582964 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.27272727 0.25 0.2 0.27272727 0.06666667 0.08333333 0.4 0.25 0.2 0.18181818] mean value: 0.2177272727272727 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.3 MCC on Training: 0.35 Running classifier: 3 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.04844975 0.04358602 0.04386926 0.05074644 0.04546833 0.04458761 0.04110742 0.04998851 0.04522514 0.04842734] mean value: 0.04614558219909668 key: score_time value: [0.00911021 0.00899315 0.0092299 0.01018214 0.01002097 0.01007962 0.00910282 0.01046968 0.00967836 0.00998569] mean value: 0.009685254096984864 key: test_mcc value: [0.38302888 0.40651745 0.02833469 0.2366834 0.1135332 0.2132685 0.29655172 0.07952363 0.17931034 0.24346972] mean value: 0.2180221535420272 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.47619048 0.5 0.18181818 0.33333333 0.26086957 0.31578947 0.4 0.24 0.3 0.36363636] mean value: 0.33716373938799565 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.45454545 0.42857143 0.16666667 0.375 0.25 0.375 0.4 0.2 0.3 0.33333333] mean value: 0.3283116883116883 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 0.6 0.2 0.3 0.27272727 0.27272727 0.4 0.3 0.3 0.4 ] mean value: 0.35454545454545455 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.84057971 0.82608696 0.73913043 0.82608696 0.75362319 0.8115942 0.82352941 0.72058824 0.79411765 0.79411765] mean value: 0.7929454390451832 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.69915254 0.73220339 0.51525424 0.60762712 0.55877743 0.59326019 0.64827586 0.54655172 0.58965517 0.63103448] mean value: 0.6121792147069762 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.3125 0.33333333 0.1 0.2 0.15 0.1875 0.25 0.13636364 0.17647059 0.22222222] mean value: 0.20683897801544862 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.29 MCC on Training: 0.22 Running classifier: 4 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.01160288 0.01037765 0.01088953 0.01145339 0.01135445 0.01094007 0.01092386 0.01033854 0.0102818 0.0108707 ] mean value: 0.010903286933898925 key: score_time value: [0.00891662 0.00887799 0.00851583 0.00894713 0.00933433 0.00870371 0.00886774 0.00878263 0.00900555 0.00862336] mean value: 0.008857488632202148 key: test_mcc value: [ 0.38188747 0.20728266 -0.1594482 -0.05254237 -0.03405127 0.2132685 0.38139946 -0.05517241 -0.03964254 -0.15161961] mean value: 0.06913616827675548 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.48 0.31578947 0. 0.1 0.10526316 0.31578947 0.47619048 0.1 0.10526316 0. ] mean value: 0.19982957393483708 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.4 0.33333333 0. 0.1 0.125 0.375 0.45454545 0.1 0.11111111 0. ] mean value: 0.19989898989898994 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.6 0.3 0. 0.1 0.09090909 0.27272727 0.5 0.1 0.1 0. ] mean value: 0.20636363636363636 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8115942 0.8115942 0.72463768 0.73913043 0.75362319 0.8115942 0.83823529 0.73529412 0.75 0.73529412] mean value: 0.7710997442455244 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.72372881 0.59915254 0.42372881 0.47372881 0.48510972 0.59326019 0.69827586 0.47241379 0.48103448 0.43103448] mean value: 0.5381467509696616 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.31578947 0.1875 0. 0.05263158 0.05555556 0.1875 0.3125 0.05263158 0.05555556 0. ] mean value: 0.12196637426900585 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.0 MCC on Training: 0.07 Running classifier: 5 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.14180589 0.13995743 0.13899994 0.14089155 0.14189243 0.14292669 0.13971615 0.13958597 0.14125586 0.14124632] mean value: 0.1408278226852417 key: score_time value: [0.01816845 0.01818967 0.01819515 0.01986742 0.01823783 0.01895308 0.01803041 0.01842952 0.01814318 0.01797581] mean value: 0.018419051170349122 key: test_mcc value: [ 0.31598405 0.20252642 0.11410535 -0.04992517 0.27846024 -0.05281143 0.17347635 0.1129932 0.31519155 0.29422298] mean value: 0.17042235375440112 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.30769231 0.26666667 0.15384615 0. 0.16666667 0. 0.16666667 0.15384615 0.30769231 0.18181818] mean value: 0.1704895104895105 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.66666667 0.4 0.33333333 0. 1. 0. 0.5 0.33333333 0.66666667 1. ] mean value: 0.49000000000000005 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.2 0.2 0.1 0. 0.09090909 0. 0.1 0.1 0.2 0.1 ] mean value: 0.1090909090909091 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.86956522 0.84057971 0.84057971 0.84057971 0.85507246 0.82608696 0.85294118 0.83823529 0.86764706 0.86764706] mean value: 0.8498934356351235 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.59152542 0.57457627 0.53305085 0.49152542 0.54545455 0.49137931 0.54137931 0.53275862 0.59137931 0.55 ] mean value: 0.5443029063280378 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.18181818 0.15384615 0.08333333 0. 0.09090909 0. 0.09090909 0.08333333 0.18181818 0.1 ] mean value: 0.0965967365967366 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.13 MCC on Training: 0.17 Running classifier: 6 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) [0.84280634 0.86029267 0.86907601 0.8623333 0.84239745 0.87277985 0.88616014 0.88221335 0.87518883 0.84378648] mean value: 0.863703441619873 key: score_time value: [0.00913501 0.00974011 0.01034331 0.00930262 0.00977826 0.01028562 0.0103941 0.00944662 0.00912714 0.00924253] mean value: 0.009679532051086426 key: test_mcc value: [0.31598405 0.25021503 0.31127585 0.17426801 0.10128837 0.23079736 0.42560808 0.42560808 0.5173899 0.36028937] mean value: 0.3112724103016343 key: train_mcc value: [0.92202689 0.95485931 0.93522428 0.93522428 0.90111525 0.92796165 0.90876037 0.94834852 0.92865484 0.89537736] mean value: 0.9257552750964202 key: test_fscore value: [0.30769231 0.28571429 0.375 0.16666667 0.14285714 0.26666667 0.42857143 0.42857143 0.46153846 0.4 ] mean value: 0.32632783882783883 key: train_fscore value: [0.93023256 0.96045198 0.94252874 0.94252874 0.91017964 0.93567251 0.91764706 0.95454545 0.93641618 0.9047619 ] mean value: 0.9334964765245465 key: test_precision value: [0.66666667 0.5 0.5 0.5 0.33333333 0.5 0.75 0.75 1. 0.6 ] mean value: 0.61 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.2 0.2 0.3 0.1 0.09090909 0.18181818 0.3 0.3 0.3 0.3 ] mean value: 0.2272727272727273 key: train_recall value: [0.86956522 0.92391304 0.89130435 0.89130435 0.83516484 0.87912088 0.84782609 0.91304348 0.88043478 0.82608696] mean value: 0.8757763975155278 key: test_accuracy value: [0.86956522 0.85507246 0.85507246 0.85507246 0.82608696 0.84057971 0.88235294 0.88235294 0.89705882 0.86764706] mean value: 0.8630861040068203 key: train_accuracy value: [0.98055105 0.98865478 0.98379254 0.98379254 0.97568882 0.9821718 0.97734628 0.98705502 0.98220065 0.97411003] mean value: 0.9815363513818298 key: test_roc_auc value: [0.59152542 0.58305085 0.62457627 0.54152542 0.52821317 0.57366771 0.64137931 0.64137931 0.65 0.63275862] mean value: 0.6008076085223952 key: train_roc_auc value: [0.93478261 0.96195652 0.94565217 0.94565217 0.91758242 0.93956044 0.92391304 0.95652174 0.94021739 0.91304348] mean value: 0.9378881987577639 key: test_jcc value: [0.18181818 0.16666667 0.23076923 0.09090909 0.07692308 0.15384615 0.27272727 0.27272727 0.3 0.25 ] mean value: 0.19963869463869466 key: train_jcc value: [0.86956522 0.92391304 0.89130435 0.89130435 0.83516484 0.87912088 0.84782609 0.91304348 0.88043478 0.82608696] mean value: 0.8757763975155278 MCC on Blind test: 0.27 MCC on Training: 0.31 Running classifier: 7 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.01021886 0.01117325 0.01155186 0.01116824 0.01150537 0.01158381 0.01085544 0.01207781 0.01131248 0.01199007] mean value: 0.011343717575073242 key: score_time value: [0.00884581 0.01199198 0.00987864 0.00930953 0.00977802 0.01003289 0.00899029 0.00971889 0.00936937 0.00973082] mean value: 0.009764623641967774 key: test_mcc value: [0.43340329 0.26452937 0.232889 0.11792374 0.15808353 0.1185096 0.31557282 0.30155466 0.31557282 0.14383899] mean value: 0.2401877835410388 key: train_mcc value: [0.28792965 0.332167 0.37065496 0.3619666 0.32435267 0.3188424 0.31020832 0.27830606 0.34298268 0.28827899] mean value: 0.3215689320941514 key: test_fscore value: [0.51851852 0.38709677 0.36363636 0.28571429 0.32258065 0.28571429 0.42857143 0.41176471 0.42857143 0.2962963 ] mean value: 0.37284647322597986 key: train_fscore value: [0.40944882 0.44194757 0.47244094 0.46323529 0.43445693 0.42807018 0.42490842 0.40145985 0.45038168 0.40875912] mean value: 0.43351088101177215 key: test_precision value: [0.41176471 0.28571429 0.26086957 0.2 0.25 0.23529412 0.33333333 0.29166667 0.33333333 0.23529412] mean value: 0.2837270125441481 key: train_precision value: [0.32098765 0.33714286 0.37037037 0.35 0.32954545 0.31443299 0.32044199 0.3021978 0.34705882 0.30769231] mean value: 0.329987024844019 key: test_recall value: [0.7 0.6 0.6 0.5 0.45454545 0.36363636 0.6 0.7 0.6 0.4 ] mean value: 0.5518181818181818 key: train_recall value: [0.56521739 0.64130435 0.65217391 0.68478261 0.63736264 0.67032967 0.63043478 0.59782609 0.64130435 0.60869565] mean value: 0.632943143812709 key: test_accuracy value: [0.8115942 0.72463768 0.69565217 0.63768116 0.69565217 0.71014493 0.76470588 0.70588235 0.76470588 0.72058824] mean value: 0.7231244671781757 key: train_accuracy value: [0.75688817 0.75850891 0.7828201 0.76337115 0.75526742 0.73581848 0.74595469 0.73462783 0.76699029 0.73786408] mean value: 0.7538111123349751 key: test_roc_auc value: [0.76525424 0.67288136 0.6559322 0.58050847 0.59796238 0.56974922 0.69655172 0.70344828 0.69655172 0.58793103] mean value: 0.6526770628553211 key: train_roc_auc value: [0.67784679 0.71017598 0.7289441 0.73096273 0.70651402 0.70873898 0.69829724 0.67819061 0.71513887 0.68457596] mean value: 0.7039385282706494 key: test_jcc value: [0.35 0.24 0.22222222 0.16666667 0.19230769 0.16666667 0.27272727 0.25925926 0.27272727 0.17391304] mean value: 0.2316490096055313 key: train_jcc value: [0.25742574 0.28365385 0.30927835 0.30143541 0.27751196 0.27232143 0.26976744 0.25114155 0.29064039 0.25688073] mean value: 0.27700568586415536 MCC on Blind test: 0.24 MCC on Training: 0.24 Running classifier: 8 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.30546236 0.28439116 0.25112391 0.29185414 0.30576038 0.19784045 0.20069313 0.2529788 0.23759174 0.2883637 ] mean value: 0.26160597801208496 key: score_time value: [0.03142905 0.02753401 0.02729177 0.02712154 0.03061008 0.02774572 0.01644111 0.01629043 0.02699924 0.02700329] mean value: 0.02584662437438965 key: test_mcc value: [ 0. 0.17426801 -0.04992517 -0.04992517 0. -0.05281143 0.31519155 -0.0507281 -0.07228181 0.29422298] mean value: 0.05080108676753788 key: train_mcc value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) [0.62896695 0.62051763 0.61198144 0.62896695 0.62451167 0.64143531 0.60340896 0.64567261 0.60340896 0.62057172] mean value: 0.6229442202569421 key: test_fscore value: [0. 0.16666667 0. 0. 0. 0. 0.30769231 0. 0. 0.18181818] mean value: 0.06561771561771561 key: train_fscore value: [0.60606061 0.59541985 0.58461538 0.60606061 0.6 0.62121212 0.57364341 0.62686567 0.57364341 0.59541985] mean value: 0.5982940905952424 key: test_precision value: [0. 0.5 0. 0. 0. 0. 0.66666667 0. 0. 1. ] mean value: 0.21666666666666665 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0. 0.1 0. 0. 0. 0. 0.2 0. 0. 0.1] mean value: 0.04 key: train_recall value: [0.43478261 0.42391304 0.41304348 0.43478261 0.42857143 0.45054945 0.40217391 0.45652174 0.40217391 0.42391304] mean value: 0.42704252269469667 key: test_accuracy value: [0.85507246 0.85507246 0.84057971 0.84057971 0.84057971 0.82608696 0.86764706 0.83823529 0.82352941 0.86764706] mean value: 0.8455029838022166 key: train_accuracy value: [0.91572123 0.91410049 0.91247974 0.91572123 0.91572123 0.91896272 0.91100324 0.91909385 0.91100324 0.91423948] mean value: 0.9148046450881969 key: test_roc_auc value: [0.5 0.54152542 0.49152542 0.49152542 0.5 0.49137931 0.59137931 0.49137931 0.48275862 0.55 ] mean value: 0.5131472822910579 key: train_roc_auc value: [0.7173913 0.71195652 0.70652174 0.7173913 0.71428571 0.72527473 0.70108696 0.72826087 0.70108696 0.71195652] mean value: 0.7135212613473483 key: test_jcc value: [0. 0.09090909 0. 0. 0. 0. 0.18181818 0. 0. 0.1 ] mean value: 0.03727272727272727 key: train_jcc value: [0.43478261 0.42391304 0.41304348 0.43478261 0.42857143 0.45054945 0.40217391 0.45652174 0.40217391 0.42391304] mean value: 0.42704252269469667 MCC on Blind test: 0.03 MCC on Training: 0.05 Running classifier: 9 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.01322031 0.00985694 0.0094583 0.01149535 0.01091671 0.01143098 0.01109552 0.01117587 0.0108583 0.0111413 ] mean value: 0.011064958572387696 key: score_time value: [0.03021383 0.0149703 0.0134182 0.01513624 0.01349139 0.01448512 0.01415443 0.01357841 0.01444769 0.01386929] mean value: 0.01577649116516113 key: test_mcc value: [ 0.11410535 0. 0.41966582 0. 0.16074413 -0.05281143 0.31519155 -0.0507281 -0.0507281 0.17347635] mean value: 0.10289155721648346 key: train_mcc value: [0.27525192 0.23531325 0.26311112 0.29411574 0.26980361 0.30550045 0.33672628 0.25195549 0.29097508 0.25195549] mean value: 0.2774708403854248 key: test_fscore value: [0.15384615 0. 0.33333333 0. 0.15384615 0. 0.30769231 0. 0. 0.16666667] mean value: 0.11153846153846154 key: train_fscore value: [0.22222222 0.20183486 0.22018349 0.25225225 0.23636364 0.21359223 0.29824561 0.21818182 0.23853211 0.21818182] mean value: 0.231959005296214 key: test_precision value: [0.33333333 0. 1. 0. 0.5 0. 0.66666667 0. 0. 0.5 ] mean value: 0.3 key: train_precision value: [0.75 0.64705882 0.70588235 0.73684211 0.68421053 0.91666667 0.77272727 0.66666667 0.76470588 0.66666667] mean value: 0.731142696312975 key: test_recall value: [0.1 0. 0.2 0. 0.09090909 0. 0.2 0. 0. 0.1 ] mean value: 0.06909090909090909 key: train_recall value: [0.13043478 0.11956522 0.13043478 0.15217391 0.14285714 0.12087912 0.18478261 0.13043478 0.14130435 0.13043478] mean value: 0.13833014811275682 key: test_accuracy value: [0.84057971 0.85507246 0.88405797 0.85507246 0.84057971 0.82608696 0.86764706 0.83823529 0.83823529 0.85294118] mean value: 0.8498508098891732 key: train_accuracy value: [0.86385737 0.85899514 0.86223663 0.86547812 0.86385737 0.86871961 0.87055016 0.86084142 0.86569579 0.86084142] mean value: 0.8641073048942319 key: test_roc_auc value: [0.53305085 0.5 0.6 0.5 0.53683386 0.49137931 0.59137931 0.49137931 0.49137931 0.54137931] mean value: 0.5276781254981138 key: train_roc_auc value: [0.56140787 0.55406832 0.56045549 0.57132505 0.56572515 0.55948899 0.58763845 0.55951397 0.56684989 0.55951397] mean value: 0.5645987151934415 key: test_jcc value: [0.08333333 0. 0.2 0. 0.08333333 0. 0.18181818 0. 0. 0.09090909] mean value: 0.06393939393939393 key: train_jcc value: [0.125 0.1122449 0.12371134 0.1443299 0.13402062 0.11956522 0.17525773 0.12244898 0.13541667 0.12244898] mean value: 0.13144443288296942 MCC on Blind test: 0.07 MCC on Training: 0.1 Running classifier: 10 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.04628539 0.0914557 0.1009922 0.09156823 0.07953119 0.05135894 0.07756495 0.08668971 0.06973839 0.09979153] mean value: 0.07949762344360352 key: score_time value: [0.01891971 0.01902604 0.02330422 0.02068472 0.01213884 0.01893759 0.01690936 0.01239729 0.01219392 0.01895809] mean value: 0.017346978187561035 key: test_mcc value: [ 0.18135593 0.27056508 0.08503904 0.17426801 -0.09284767 0.11592434 0.26926311 0.13454906 0.13262213 0.1558763 ] mean value: 0.14266153329533873 key: train_mcc value: [0.49499622 0.52663856 0.51760479 0.5677723 0.51954459 0.52991198 0.49010257 0.53841789 0.52341686 0.48893216] mean value: 0.5197337908088964 key: test_fscore value: [0.3 0.38095238 0.21052632 0.16666667 0. 0.22222222 0.35294118 0.27272727 0.23529412 0.28571429] mean value: 0.24270444381899486 key: train_fscore value: [0.53691275 0.57861635 0.56962025 0.61146497 0.575 0.5751634 0.54193548 0.57894737 0.57324841 0.53333333] mean value: 0.5674242317158009 key: test_precision value: [0.3 0.36363636 0.22222222 0.5 0. 0.28571429 0.42857143 0.25 0.28571429 0.27272727] mean value: 0.29085858585858587 key: train_precision value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) [0.70175439 0.68656716 0.68181818 0.73846154 0.66666667 0.70967742 0.66666667 0.73333333 0.69230769 0.68965517] mean value: 0.6966908221166728 key: test_recall value: [0.3 0.4 0.2 0.1 0. 0.18181818 0.3 0.3 0.2 0.3 ] mean value: 0.22818181818181818 key: train_recall value: [0.43478261 0.5 0.48913043 0.52173913 0.50549451 0.48351648 0.45652174 0.47826087 0.48913043 0.43478261] mean value: 0.4793358815097945 key: test_accuracy value: [0.79710145 0.8115942 0.7826087 0.85507246 0.79710145 0.79710145 0.83823529 0.76470588 0.80882353 0.77941176] mean value: 0.8031756180733163 key: train_accuracy value: [0.88816856 0.89141005 0.8897893 0.90113452 0.8897893 0.89465154 0.88511327 0.89644013 0.89158576 0.88673139] mean value: 0.8914813824067809 key: test_roc_auc value: [0.59067797 0.64067797 0.54067797 0.54152542 0.47413793 0.54780564 0.61551724 0.57241379 0.55689655 0.58103448] mean value: 0.5661364964667127 key: train_roc_auc value: [0.70120083 0.73 0.72456522 0.74467909 0.73088413 0.72464798 0.70829889 0.72392131 0.72555381 0.70028104] mean value: 0.7214032295472204 key: test_jcc value: [0.17647059 0.23529412 0.11764706 0.09090909 0. 0.125 0.21428571 0.15789474 0.13333333 0.16666667] mean value: 0.14175013067427927 key: train_jcc value: [0.36697248 0.40707965 0.39823009 0.44036697 0.40350877 0.40366972 0.37168142 0.40740741 0.40178571 0.36363636] mean value: 0.39643385820137145 MCC on Blind test: 0.24 MCC on Training: 0.14 Running classifier: 11 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.05038977 0.04330969 0.04155087 0.04175973 0.04769397 0.04122114 0.04179716 0.04145861 0.04249144 0.04223228] mean value: 0.04339046478271484 key: score_time value: [0.01211405 0.01236248 0.01277256 0.01207685 0.01234269 0.01237202 0.01248622 0.01219058 0.01241279 0.01243758] mean value: 0.012356781959533691 key: test_mcc value: [ 0.17426801 0.16516678 -0.04992517 -0.04992517 -0.05281143 -0.09284767 0.41923451 0.1129932 -0.0507281 0.20120056] mean value: 0.0776625521970544 key: train_mcc value: [0.24524742 0.30223086 0.33299501 0.29231356 0.31321636 0.35225325 0.21407875 0.28627139 0.29730396 0.32040736] mean value: 0.2956317917533605 key: test_fscore value: [0.16666667 0.25 0. 0. 0. 0. 0.33333333 0.15384615 0. 0.26666667] mean value: 0.11705128205128205 key: train_fscore value: [0.24137931 0.3 0.33870968 0.27586207 0.30508475 0.34710744 0.22222222 0.29508197 0.28813559 0.32520325] mean value: 0.29387862751971366 key: test_precision value: [0.5 0.33333333 0. 0. 0. 0. 1. 0.33333333 0. 0.4 ] mean value: 0.25666666666666665 key: train_precision value: [0.58333333 0.64285714 0.65625 0.66666667 0.66666667 0.7 0.52 0.6 0.65384615 0.64516129] mean value: 0.6334781253692545 key: test_recall value: [0.1 0.2 0. 0. 0. 0. 0.2 0.1 0. 0.2] mean value: 0.08 key: train_recall value: [0.15217391 0.19565217 0.22826087 0.17391304 0.1978022 0.23076923 0.14130435 0.19565217 0.18478261 0.2173913 ] mean value: 0.19177018633540371 key: test_accuracy value: [0.85507246 0.82608696 0.84057971 0.84057971 0.82608696 0.79710145 0.88235294 0.83823529 0.83823529 0.83823529] mean value: 0.8382566069906223 key: train_accuracy value: [0.85737439 0.86385737 0.86709887 0.86385737 0.86709887 0.8719611 0.85275081 0.86084142 0.86407767 0.86569579] mean value: 0.8634613669860951 key: test_roc_auc value: [0.54152542 0.56610169 0.49152542 0.49152542 0.49137931 0.47413793 0.6 0.53275862 0.49137931 0.57413793] mean value: 0.5254471069549971 key: train_roc_auc value: [0.56656315 0.58830228 0.60365424 0.57933747 0.59034597 0.60682948 0.55924533 0.58641924 0.58383617 0.59823938] mean value: 0.5862772713317431 key: test_jcc value: [0.09090909 0.14285714 0. 0. 0. 0. 0.2 0.08333333 0. 0.15384615] mean value: 0.06709457209457209 key: train_jcc value: [0.1372549 0.17647059 0.2038835 0.16 0.18 0.21 0.125 0.17307692 0.16831683 0.19417476] mean value: 0.1728177497383354 MCC on Blind test: 0.28 MCC on Training: 0.08 Running classifier: 12 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(cv=3, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(cv=3, random_state=42))]) key: fit_time value: [0.58705235 0.79099607 0.48450661 0.64903235 0.50716043 0.67958879 0.52808309 0.50340223 0.54245591 0.61110497] mean value: 0.5883382797241211 key: score_time value: [0.01429868 0.01266384 0.01253462 0.01265836 0.01375103 0.01319861 0.01332688 0.0133183 0.01244497 0.01385498] mean value: 0.01320502758026123 key: test_mcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_mcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_fscore value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_fscore value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_precision value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_precision value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_recall value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_recall value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_accuracy value: [0.85507246 0.85507246 0.85507246 0.85507246 0.84057971 0.84057971 0.85294118 0.85294118 0.85294118 0.85294118] mean value: 0.8513213981244672 key: train_accuracy value: [0.85089141 0.85089141 0.85089141 0.85089141 0.85251216 0.85251216 0.85113269 0.85113269 0.85113269 0.85113269] mean value: 0.8513120695714204 key: test_roc_auc value: [0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5] mean value: 0.5 key: train_roc_auc value: [0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5] mean value: 0.5 key: test_jcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_jcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 MCC on Blind test: 0.0 MCC on Training: 0.0 Running classifier: 13 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [2.07964706 1.37202573 0.99014306 0.71773386 0.63963795 1.20504904 0.60029292 2.26780963 1.2254827 0.68593669] mean value: 1.1783758640289306 key: score_time value: [0.01288056 0.01250553 0.01283002 0.01307082 0.01314306 0.01312947 0.01263094 0.0126574 0.0127492 0.02204013] mean value: 0.013763713836669921 key: test_mcc value: [ 0.31127585 0.16516678 0.17426801 0. 0. -0.09284767 0.29422298 0.24913644 0.36028937 0.1129932 ] mean value: 0.1574504959392407 key: train_mcc value: [0.58253578 0.56091885 0.46603662 0.26013136 0.2496289 0.36776922 0.17085731 0.63601557 0.5169558 0.32418136] mean value: 0.41350307821699 key: test_fscore value: [0.375 0.25 0.16666667 0. 0. 0. 0.18181818 0.28571429 0.4 0.15384615] mean value: 0.1813045288045288 key: train_fscore value: [0.58394161 0.58333333 0.4964539 0.25641026 0.23214286 0.34188034 0.16363636 0.62222222 0.5620915 0.31666667] mean value: 0.4158779051108651 key: test_precision value: [0.5 0.33333333 0.5 0. 0. 0. 1. 0.5 0.6 0.33333333] mean value: 0.37666666666666665 key: train_precision value: [0.88888889 0.80769231 0.71428571 0.6 0.61904762 0.76923077 0.5 0.97674419 0.70491803 0.67857143] mean value: 0.7259378946550126 key: test_recall value: [0.3 0.2 0.1 0. 0. 0. 0.1 0.2 0.3 0.1] mean value: 0.13 key: train_recall value: [0.43478261 0.45652174 0.38043478 0.16304348 0.14285714 0.21978022 0.09782609 0.45652174 0.4673913 0.20652174] mean value: 0.3025680840898232 key: test_accuracy value: [0.85507246 0.82608696 0.85507246 0.85507246 0.84057971 0.79710145 0.86764706 0.85294118 0.86764706 0.83823529] mean value: 0.845545609548167 key: train_accuracy value: [0.9076175 0.90275527 0.88492707 0.85899514 0.86061588 0.87520259 0.85113269 0.91747573 0.89158576 0.86731392] mean value: 0.8817621542802894 key: test_roc_auc value: [0.62457627 0.56610169 0.54152542 0.5 0.5 0.47413793 0.55 0.58275862 0.63275862 0.53275862] mean value: 0.5504617182933957 key: train_roc_auc value: [0.7126294 0.71873706 0.67688406 0.57199793 0.56382401 0.60418669 0.54035791 0.7273103 0.71658539 0.59470574] mean value: 0.6427218475853788 key: test_jcc value: [0.23076923 0.14285714 0.09090909 0. 0. 0. 0.1 0.16666667 0.25 0.08333333] mean value: 0.10645354645354646 key: train_jcc value: [0.41237113 0.41176471 0.33018868 0.14705882 0.13131313 0.20618557 0.08910891 0.4516129 0.39090909 0.18811881] mean value: 0.2758631757908282 MCC on Blind test: 0.15 MCC on Training: 0.16 Running classifier: 14 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.01420927 0.01393175 0.01128387 0.00972176 0.01078081 0.01045442 0.0106833 0.01014781 0.00995493 0.01095176] mean value: 0.011211967468261719 key: score_time value: [0.01200747 0.0101223 0.00946212 0.00892663 0.00923848 0.00877309 0.00893855 0.00872993 0.00888419 0.00887275] mean value: 0.009395551681518555 key: test_mcc value: [ 0.31598405 0.41966582 -0.04992517 0. -0.05281143 -0.05281143 0. 0.1129932 0.17347635 -0.0507281 ] mean value: 0.08158432943651608 key: train_mcc value: [0.08114377 0.10087818 0.19981631 0.17027242 0.16176045 0.1821722 0.12876075 0.10094507 0.13881208 0.11560272] mean value: 0.1380163949269465 key: test_fscore value: [0.30769231 0.33333333 0. 0. 0. 0. 0. 0.15384615 0.16666667 0. ] mean value: 0.09615384615384615 key: train_fscore value: [0.0776699 0.07920792 0.1682243 0.14953271 0.13461538 0.15238095 0.11428571 0.07920792 0.11538462 0.09708738] mean value: 0.11675967991500176 key: test_precision value: [0.66666667 1. 0. 0. 0. 0. 0. 0.33333333 0.5 0. ] mean value: 0.25 key: train_precision value: [0.36363636 0.44444444 0.6 0.53333333 0.53846154 0.57142857 0.46153846 0.44444444 0.5 0.45454545] mean value: 0.49118326118326106 key: test_recall value: [0.2 0.2 0. 0. 0. 0. 0. 0.1 0.1 0. ] mean value: 0.06 key: train_recall value: [0.04347826 0.04347826 0.09782609 0.08695652 0.07692308 0.08791209 0.06521739 0.04347826 0.06521739 0.05434783] mean value: 0.06648351648351648 key: test_accuracy value: [0.86956522 0.88405797 0.84057971 0.85507246 0.82608696 0.82608696 0.85294118 0.83823529 0.85294118 0.83823529] mean value: 0.8483802216538789 key: train_accuracy value: [0.84602917 0.84927066 0.85575365 0.85251216 0.8541329 0.85575365 0.84951456 0.84951456 0.85113269 0.84951456] mean value: 0.8513128563411014 key: test_roc_auc value: [0.59152542 0.6 0.49152542 0.5 0.49137931 0.49137931 0.5 0.53275862 0.54137931 0.49137931] mean value: 0.5231326709526593 key: train_roc_auc value: [0.51507246 0.51697723 0.54319876 0.53681159 0.53275812 0.53825262 0.5259547 0.51698628 0.52690527 0.52147049] mean value: 0.5274387526288666 key: test_jcc value: [0.18181818 0.2 0. 0. 0. 0. 0. 0.08333333 0.09090909 0. ] mean value: 0.0556060606060606 key: train_jcc value: [0.04040404 0.04123711 0.09183673 0.08080808 0.07216495 0.08247423 0.06060606 0.04123711 0.06122449 0.05102041] mean value: 0.062301321653309885 MCC on Blind test: 0.19 MCC on Training: 0.08 Running classifier: 15 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.01253796 0.01039934 0.01104259 0.01106882 0.01068807 0.01190376 0.01147413 0.01141238 0.01079249 0.01135397] mean value: 0.011267352104187011 key: score_time value: [0.00889635 0.00876832 0.00887609 0.00909233 0.00948191 0.00923443 0.00927877 0.00883579 0.00911784 0.00920439] mean value: 0.009078621864318848 key: test_mcc value: [-0.04992517 -0.04992517 0.36527517 0.17426801 -0.07524193 0.06138228 -0.10380685 0.04211174 0.04211174 0.29422298] mean value: 0.07004728142356742 key: train_mcc value: [0.15367352 0.18361415 0.13288436 0.15813497 0.16521661 0.17463304 0.19814227 0.17820546 0.14365033 0.16896906] mean value: 0.16571237683144513 key: test_fscore value: [0. 0. 0.44444444 0.16666667 0. 0.13333333 0. 0.13333333 0.13333333 0.18181818] mean value: 0.1192929292929293 key: train_fscore value: [0.19512195 0.224 0.16806723 0.20634921 0.20967742 0.22222222 0.23809524 0.22222222 0.18181818 0.20967742] mean value: 0.20772510875270167 key: test_precision value: [0. 0. 0.5 0.5 0. 0.25 0. 0.2 0.2 1. ] mean value: 0.265 key: train_precision value: [0.38709677 0.42424242 0.37037037 0.38235294 0.39393939 0.4 0.44117647 0.41176471 0.37931034 0.40625 ] mean value: 0.39965034252203824 key: test_recall value: [0. 0. 0.4 0.1 0. 0.09090909 0. 0.1 0.1 0.1 ] mean value: 0.08909090909090908 key: train_recall value: [0.13043478 0.15217391 0.10869565 0.14130435 0.14285714 0.15384615 0.16304348 0.15217391 0.11956522 0.14130435] mean value: 0.14053989488772098 key: test_accuracy value: [0.84057971 0.84057971 0.85507246 0.85507246 0.8115942 0.8115942 0.79411765 0.80882353 0.80882353 0.86764706] mean value: 0.8293904518329069 key: train_accuracy value: [0.83954619 0.84278768 0.83954619 0.83792545 0.84116694 0.84116694 0.84466019 0.84142395 0.83980583 0.84142395] mean value: 0.8409453299974299 key: test_roc_auc value: [0.49152542 0.49152542 0.66610169 0.54152542 0.48275862 0.51959248 0.46551724 0.51551724 0.51551724 0.55 ] mean value: 0.523958078741831 key: train_roc_auc value: [0.54712215 0.55799172 0.53815735 0.55065217 0.55241716 0.5569611 0.5634609 0.55707555 0.54267234 0.55259134] mean value: 0.551910179201963 key: test_jcc value: [0. 0. 0.28571429 0.09090909 0. 0.07142857 0. 0.07142857 0.07142857 0.1 ] mean value: 0.06909090909090908 key: train_jcc value: [0.10810811 0.12612613 0.09174312 0.11504425 0.11711712 0.125 0.13513514 0.125 0.1 0.11711712] mean value: 0.11603909706572693 MCC on Blind test: 0.18 MCC on Training: 0.07 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.01235986 0.02240014 0.01854968 0.02304292 0.01986241 0.01949143 0.01958871 0.02168465 0.01991868 0.0203054 ] mean value: 0.01972038745880127 key: score_time value: [0.01008105 0.0119586 0.0117929 0.01207185 0.01201653 0.01201868 0.01202798 0.01202798 0.01197815 0.01231194] mean value: 0.01182856559753418 key: test_mcc value: [ 0.17426801 0.31598405 -0.0711298 0.1639963 0.13019032 0.10128837 0.31001094 -0.04311472 -0.0507281 0.13262213] mean value: 0.1163387496181159 key: train_mcc value: [0.25187991 0.23758583 0.31765964 0.44381317 0.29460218 0.31819482 0.38003908 0.19688345 0.18436354 0.31776317] mean value: 0.29427847719727185 key: test_fscore value: [0.16666667 0.30769231 0. 0.30769231 0.31372549 0.14285714 0.375 0.23880597 0. 0.23529412] mean value: 0.20877340029008157 key: train_fscore value: [0.21818182 0.17307692 0.33333333 0.51929825 0.37610619 0.29565217 0.44871795 0.31195841 0.13592233 0.33333333] mean value: 0.31455807065037156 key: test_precision value: [0.5 0.66666667 0. 0.25 0.2 0.33333333 0.5 0.14035088 0. 0.28571429] mean value: 0.28760651629072675 key: train_precision value: [0.66666667 0.75 0.61764706 0.38341969 0.23545706 0.70833333 0.546875 0.18556701 0.63636364 0.61764706] mean value: 0.5347976517151055 key: test_recall value: [0.1 0.2 0. 0.4 0.72727273 0.09090909 0.3 0.8 0. 0.2 ] mean value: 0.28181818181818186 key: train_recall value: [0.13043478 0.09782609 0.22826087 0.80434783 0.93406593 0.18681319 0.38043478 0.97826087 0.07608696 0.22826087] mean value: 0.4044792164357382 key: test_accuracy value: [0.85507246 0.86956522 0.82608696 0.73913043 0.49275362 0.82608696 0.85294118 0.25 0.83823529 0.80882353] mean value: 0.7358695652173912 key: train_accuracy value: [0.86061588 0.86061588 0.86385737 0.77795786 0.54294976 0.86871961 0.86084142 0.35760518 0.85598706 0.86407767] mean value: 0.7713227696390825 key: test_roc_auc value: [0.54152542 0.59152542 0.48305085 0.59830508 0.58777429 0.52821317 0.62413793 0.47758621 0.49137931 0.55689655] mean value: 0.5480394240476063 key: train_roc_auc value: [0.55950311 0.5460559 0.60174948 0.78884058 0.70467555 0.5867526 0.66265085 0.61365515 0.5342412 0.60177302] mean value: 0.6199897440136322 key: test_jcc value: [0.09090909 0.18181818 0. 0.18181818 0.18604651 0.07692308 0.23076923 0.13559322 0. 0.13333333] mean value: 0.12172108275379856 key: train_jcc value: [0.12244898 0.09473684 0.2 0.3507109 0.23160763 0.17346939 0.2892562 0.18480493 0.07291667 0.2 ] mean value: 0.19199515324991195 MCC on Blind test: 0.19 MCC on Training: 0.12 Running classifier: 17 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.03273201 0.0318687 0.0444808 0.04011536 0.05980754 0.04540682 0.04669547 0.0329299 0.03202105 0.03295183] mean value: 0.03990094661712647 key: score_time value: [0.01268578 0.01301479 0.01267314 0.01923561 0.01298141 0.01736665 0.01292133 0.01251483 0.01269007 0.01280522] mean value: 0.013888883590698241 key: test_mcc value: [-0.0711298 -0.11507183 0.25021503 0. 0.16074413 0.06138228 -0.07228181 0.04211174 -0.07228181 0.24913644] mean value: 0.04328243688214719 key: train_mcc value: [0.23672316 0.21592107 0.19296809 0.21592107 0.19421015 0.21731088 0.21595026 0.23675484 0.23675484 0.16700214] mean value: 0.21295165120901477 key: test_fscore value: [0. 0. 0.28571429 0. 0.15384615 0.13333333 0. 0.13333333 0. 0.28571429] mean value: 0.09919413919413919 key: train_fscore value: [0.12244898 0.10309278 0.08333333 0.10309278 0.08421053 0.10416667 0.10309278 0.12244898 0.12244898 0.06315789] mean value: 0.10114937103436057 key: test_precision value: [0. 0. 0.5 0. 0.5 0.25 0. 0.2 0. 0.5 ] mean value: 0.195 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0. 0. 0.2 0. 0.09090909 0.09090909 0. 0.1 0. 0.2 ] mean value: 0.06818181818181819 key: train_recall value: [0.06521739 0.05434783 0.04347826 0.05434783 0.04395604 0.05494505 0.05434783 0.06521739 0.06521739 0.0326087 ] mean value: 0.05336837075967511 key: test_accuracy value: [0.82608696 0.7826087 0.85507246 0.85507246 0.84057971 0.8115942 0.82352941 0.80882353 0.82352941 0.85294118] mean value: 0.8279838022165388 key: train_accuracy value: [0.86061588 0.85899514 0.85737439 0.85899514 0.85899514 0.86061588 0.8592233 0.86084142 0.86084142 0.85598706] mean value: 0.8592484776006671 key: test_roc_auc value: [0.48305085 0.45762712 0.58305085 0.5 0.53683386 0.51959248 0.48275862 0.51551724 0.48275862 0.58275862] mean value: 0.5143948249295998 key: train_roc_auc value: [0.5326087 0.52717391 0.52173913 0.52717391 0.52197802 0.52747253 0.52717391 0.5326087 0.5326087 0.51630435] mean value: 0.5266841853798375 key: test_jcc value: [0. 0. 0.16666667 0. 0.08333333 0.07142857 0. 0.07142857 0. 0.16666667] mean value: 0.055952380952380955 key: train_jcc value: [0.06521739 0.05434783 0.04347826 0.05434783 0.04395604 0.05494505 0.05434783 0.06521739 0.06521739 0.0326087 ] mean value: 0.05336837075967511 MCC on Blind test: -0.1 MCC on Training: 0.04 Running classifier: 18 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42))]) key: fit_time value: [0.72734189 0.76462936 0.7254703 0.74053073 0.7106626 0.75805473 0.84072685 0.75208211 0.75639105 0.70890522] mean value: 0.7484794855117798 key: score_time value: [0.15392494 0.17204523 0.14264941 0.1748507 0.21294045 0.19670272 0.17722654 0.15802693 0.12817359 0.19798493] mean value: 0.17145254611968994 key: test_mcc value: [ 0.31598405 0.07404322 0. 0. 0.27846024 -0.05281143 0.42560808 0.36028937 0.17347635 0.17347635] mean value: 0.17485262385000938 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.30769231 0.14285714 0. 0. 0.16666667 0. 0.42857143 0.4 0.16666667 0.16666667] mean value: 0.17791208791208793 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.66666667 0.25 0. 0. 1. 0. 0.75 0.6 0.5 0.5 ] mean value: 0.42666666666666664 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.2 0.1 0. 0. 0.09090909 0. 0.3 0.3 0.1 0.1 ] mean value: 0.11909090909090911 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.86956522 0.82608696 0.85507246 0.85507246 0.85507246 0.82608696 0.88235294 0.86764706 0.85294118 0.85294118] mean value: 0.8542838874680306 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.59152542 0.52457627 0.5 0.5 0.54545455 0.49137931 0.64137931 0.63275862 0.54137931 0.54137931] mean value: 0.5509832102438764 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.18181818 0.07692308 0. 0. 0.09090909 0. 0.27272727 0.25 0.09090909 0.09090909] mean value: 0.10541958041958041 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.3 MCC on Training: 0.17 Running classifier: 19 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea...age_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42))]) key: fit_time value: [1.08703041 1.10914421 1.06888628 1.10137057 1.1092701 1.0631094 1.03356671 1.13539314 1.10595512 1.15049481] mean value: 1.0964220762252808 key: score_time value: [0.25396967 0.25737357 0.29113865 0.28779125 0.20403957 0.25042987 0.26227236 0.22487831 0.17454982 0.23557115] mean value: 0.2442014217376709 key: test_mcc value: [ 0.29455849 0.17426801 0. 0. 0. -0.05281143 0.41923451 0.29422298 0. 0.29422298] mean value: 0.14236955581712007 key: train_mcc value: [0.54004172 0.58581201 0.5587053 0.58581201 0.58060044 0.60723814 0.57694178 0.59468737 0.57694178 0.57694178] mean value: 0.5783722338775406 key: test_fscore value: [0.18181818 0.16666667 0. 0. 0. 0. 0.33333333 0.18181818 0. 0.18181818] mean value: 0.10454545454545454 key: train_fscore value: [0.49180328 0.5511811 0.51612903 0.5511811 0.544 0.578125 0.53968254 0.5625 0.53968254 0.53968254] mean value: 0.5413967134718617 key: test_precision value: [1. 0.5 0. 0. 0. 0. 1. 1. 0. 1. ] mean value: 0.45 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.1 0.1 0. 0. 0. 0. 0.2 0.1 0. 0.1] mean value: 0.06 key: train_recall value: [0.32608696 0.38043478 0.34782609 0.38043478 0.37362637 0.40659341 0.36956522 0.39130435 0.36956522 0.36956522] mean value: 0.37150023889154327 key: test_accuracy value: [0.86956522 0.85507246 0.85507246 0.85507246 0.84057971 0.82608696 0.88235294 0.86764706 0.85294118 0.86764706] mean value: 0.8572037510656436 key: train_accuracy value: [0.89951378 0.9076175 0.90275527 0.9076175 0.9076175 0.91247974 0.90614887 0.90938511 0.90614887 0.90614887] mean value: 0.906543301180679 key: test_roc_auc value: [0.55 0.54152542 0.5 0.5 0.5 0.49137931 0.6 0.55 0.5 0.55 ] mean value: 0.5282904734073641 key: train_roc_auc value: [0.66304348 0.69021739 0.67391304 0.69021739 0.68681319 0.7032967 0.68478261 0.69565217 0.68478261 0.68478261] mean value: 0.6857501194457717 key: test_jcc value: [0.1 0.09090909 0. 0. 0. 0. 0.2 0.1 0. 0.1 ] mean value: 0.0590909090909091 key: train_jcc value: [0.32608696 0.38043478 0.34782609 0.38043478 0.37362637 0.40659341 0.36956522 0.39130435 0.36956522 0.36956522] mean value: 0.37150023889154327 MCC on Blind test: 0.11 MCC on Training: 0.14 Running classifier: 20 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.04603219 0.03337979 0.03963232 0.04110813 0.04092407 0.03422308 0.03931761 0.03929448 0.03956461 0.03945971] mean value: 0.03929359912872314 key: score_time value: [0.03520942 0.01859736 0.01917028 0.01871419 0.01858616 0.01859164 0.01878309 0.01865578 0.01866984 0.01857638] mean value: 0.020355415344238282 key: test_mcc value: [ 0.11410535 0.2707383 -0.04992517 0. -0.07524193 -0.05281143 0.41923451 0.04211174 0.17347635 -0.10380685] mean value: 0.07378808784756377 key: train_mcc value: [0.37756319 0.37079436 0.38001809 0.3585188 0.38558243 0.42743017 0.31994801 0.38788017 0.36523612 0.40615336] mean value: 0.3779124706325606 key: test_fscore value: [0.15384615 0.35294118 0. 0. 0. 0. 0.33333333 0.13333333 0.16666667 0. ] mean value: 0.11401206636500753 key: train_fscore value: [0.35294118 0.36065574 0.36363636 0.34710744 0.37704918 0.40983607 0.30508475 0.3559322 0.33898305 0.4 ] mean value: 0.3611225961730038 key: test_precision value: [0.33333333 0.42857143 0. 0. 0. 0. 1. 0.2 0.5 0. ] mean value: 0.2461904761904762 key: train_precision value: [0.77777778 0.73333333 0.75862069 0.72413793 0.74193548 0.80645161 0.69230769 0.80769231 0.76923077 0.75757576] mean value: 0.7569063355381487 key: test_recall value: [0.1 0.3 0. 0. 0. 0. 0.2 0.1 0.1 0. ] mean value: 0.08 key: train_recall value: [0.22826087 0.23913043 0.23913043 0.22826087 0.25274725 0.27472527 0.19565217 0.22826087 0.2173913 0.27173913] mean value: 0.23752986144290494 key: test_accuracy value: [0.84057971 0.84057971 0.84057971 0.85507246 0.8115942 0.82608696 0.88235294 0.80882353 0.85294118 0.79411765] mean value: 0.8352728047740836 key: train_accuracy value: [0.87520259 0.87358185 0.87520259 0.8719611 0.87682334 0.88330632 0.86731392 0.87702265 0.87378641 0.87864078] mean value: 0.8752841549831368 key: test_roc_auc value: [0.53305085 0.61610169 0.49152542 0.5 0.48275862 0.49137931 0.6 0.51551724 0.54137931 0.46551724] mean value: 0.5237229690239625 key: train_roc_auc value: [0.60841615 0.61194617 0.61289855 0.60651139 0.61876906 0.63165922 0.59022152 0.60937758 0.60299223 0.628265 ] mean value: 0.6121056875565059 key: test_jcc value: [0.08333333 0.21428571 0. 0. 0. 0. 0.2 0.07142857 0.09090909 0. ] mean value: 0.065995670995671 key: train_jcc value: [0.21428571 0.22 0.22222222 0.21 0.23232323 0.25773196 0.18 0.21649485 0.20408163 0.25 ] mean value: 0.22071396056079412 MCC on Blind test: 0.22 MCC on Training: 0.07 Running classifier: 21 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=3) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=3))]) key: fit_time value: [0.11801219 0.11553764 0.12363291 0.12737846 0.12985253 0.12980747 0.12349629 0.12857985 0.14150977 0.1354003 ] mean value: 0.1273207426071167 key: score_time value: [0.01895285 0.01881814 0.0236342 0.02509761 0.01904273 0.01926064 0.02222824 0.01893067 0.02295303 0.01921701] mean value: 0.020813512802124023 key: test_mcc value: [ 0.29455849 0.17426801 -0.04992517 0. -0.05281143 -0.05281143 0. 0.04211174 -0.0507281 -0.07228181] mean value: 0.02323803166247108 key: train_mcc value: [0.06366587 0.13883052 0.162476 0.16036451 0.15434493 0.42743017 0.07275203 0.38788017 0.17002001 0.11560272] mean value: 0.18533669373657663 key: test_fscore value: [0.18181818 0.16666667 0. 0. 0. 0. 0. 0.13333333 0. 0. ] mean value: 0.04818181818181818 key: train_fscore value: [0.04123711 0.0990099 0.11764706 0.13333333 0.1010101 0.40983607 0.06 0.3559322 0.1010101 0.09708738] mean value: 0.15161032561736032 key: test_precision value: [1. 0.5 0. 0. 0. 0. 0. 0.2 0. 0. ] mean value: 0.16999999999999998 key: train_precision value: [0.4 0.55555556 0.6 0.53846154 0.625 0.80645161 0.375 0.80769231 0.71428571 0.45454545] mean value: 0.5876992183443797 key: test_recall value: [0.1 0.1 0. 0. 0. 0. 0. 0.1 0. 0. ] mean value: 0.030000000000000006 key: train_recall value: [0.02173913 0.05434783 0.06521739 0.07608696 0.05494505 0.27472527 0.0326087 0.22826087 0.05434783 0.05434783] mean value: 0.09166268514094603 key: test_accuracy value: [0.86956522 0.85507246 0.84057971 0.85507246 0.82608696 0.82608696 0.85294118 0.80882353 0.83823529 0.82352941] mean value: 0.8395993179880648 key: train_accuracy value: [0.84927066 0.85251216 0.8541329 0.85251216 0.85575365 0.88330632 0.84789644 0.87702265 0.85598706 0.84951456] mean value: 0.8577908556382537 key: test_roc_auc value: [0.55 0.54152542 0.49152542 0.5 0.49137931 0.49137931 0.5 0.51551724 0.49137931 0.48275862] mean value: 0.5055464640561075 key: train_roc_auc value: [0.50801242 0.52336439 0.52879917 0.53232919 0.52462082 0.63165922 0.5115515 0.60937758 0.52527277 0.52147049] mean value: 0.5416457550274709 key: test_jcc value: [0.1 0.09090909 0. 0. 0. 0. 0. 0.07142857 0. 0. ] mean value: 0.02623376623376623 key: train_jcc value: [0.02105263 0.05208333 0.0625 0.07142857 0.05319149 0.25773196 0.03092784 0.21649485 0.05319149 0.05102041] mean value: 0.08696225624027795 MCC on Blind test: -0.03 MCC on Training: 0.02 Running classifier: 22 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.03643703 0.02307987 0.02225184 0.02260852 0.02227807 0.02207088 0.02325702 0.02992058 0.02578545 0.02761817] mean value: 0.025530743598937988 key: score_time value: [0.01895356 0.01255584 0.01233792 0.01249623 0.01226258 0.01223755 0.01228213 0.01341367 0.01343894 0.01373029] mean value: 0.013370871543884277 key: test_mcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_mcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_fscore value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_fscore value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_precision value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_precision value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_recall value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_recall value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_accuracy value: [0.85507246 0.85507246 0.85507246 0.85507246 0.84057971 0.84057971 0.85294118 0.85294118 0.85294118 0.85294118] mean value: 0.8513213981244672 key: train_accuracy value: [0.85089141 0.85089141 0.85089141 0.85089141 0.85251216 0.85251216 0.85113269 0.85113269 0.85113269 0.85113269] mean value: 0.8513120695714204 key: test_roc_auc value: [0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5] mean value: 0.5 key: train_roc_auc value: [0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5] mean value: 0.5 key: test_jcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_jcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 MCC on Blind test: 0.0 MCC on Training: 0.0 Running classifier: 23 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.014292 0.0265348 0.02473187 0.02832341 0.02780223 0.02460647 0.02695918 0.02716208 0.03145552 0.03206253] mean value: 0.0263930082321167 key: score_time value: [0.01014304 0.01152873 0.0119679 0.01202893 0.01177764 0.01181459 0.01175022 0.01164794 0.01176476 0.01184464] mean value: 0.011626839637756348 key: test_mcc value: [0.45664488 0.20252642 0. 0. 0.14661427 0.15155795 0.17347635 0.17931034 0.30155466 0.4800871 ] mean value: 0.20917719799311968 key: train_mcc value: [0.45025279 0.26311112 0.35243966 0. 0.51461168 0.41314354 0.38593403 0.49303319 0.41507754 0.51471318] mean value: 0.38023167053756035 key: test_fscore value: [0.53846154 0.26666667 0. 0. 0.23529412 0.32432432 0.16666667 0.3 0.41176471 0.56 ]/home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:463: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_CV['source_data'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:490: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_BT['source_data'] = 'BT' mean value: 0.2803178019648608 key: train_fscore value: [0.53811659 0.22018349 0.32478632 0. 0.58285714 0.48297214 0.384 0.56321839 0.4875 0.58695652] mean value: 0.41705905945768895 key: test_precision value: [0.4375 0.4 0. 0. 0.33333333 0.23076923 0.5 0.3 0.29166667 0.46666667] mean value: 0.29599358974358975 key: train_precision value: [0.45801527 0.70588235 0.76 0. 0.60714286 0.3362069 0.72727273 0.59756098 0.34210526 0.58695652] mean value: 0.5121142861590839 key: test_recall value: [0.7 0.2 0. 0. 0.18181818 0.54545455 0.1 0.3 0.7 0.7 ] mean value: 0.34272727272727277 key: train_recall value: [0.65217391 0.13043478 0.20652174 0. 0.56043956 0.85714286 0.26086957 0.5326087 0.84782609 0.58695652] mean value: 0.4634973721930244 key: test_accuracy value: [0.82608696 0.84057971 0.85507246 0.85507246 0.8115942 0.63768116 0.85294118 0.79411765 0.70588235 0.83823529] mean value: 0.8017263427109974 key: train_accuracy value: [0.83306321 0.86223663 0.8719611 0.85089141 0.88168558 0.72933549 0.87540453 0.87702265 0.73462783 0.87702265] mean value: 0.8393251089676008 key: test_roc_auc value: [0.77372881 0.57457627 0.5 0.5 0.55642633 0.60031348 0.54137931 0.58965517 0.70344828 0.78103448] mean value: 0.6120562138037299 key: train_roc_auc value: [0.75846791 0.56045549 0.59754658 0.5 0.74885096 0.7821836 0.62187965 0.73493553 0.78132749 0.75735659] mean value: 0.6843003790170263 key: test_jcc value: [0.36842105 0.15384615 0. 0. 0.13333333 0.19354839 0.09090909 0.17647059 0.25925926 0.38888889] mean value: 0.17646767542003733 key: train_jcc value: [0.36809816 0.12371134 0.19387755 0. 0.41129032 0.31836735 0.23762376 0.392 0.32231405 0.41538462] mean value: 0.2782667147602847 MCC on Blind test: 0.12 MCC on Training: 0.21 Running classifier: 24 Model_name: XGBoost Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.1684351 0.12943912 0.12344885 0.12512064 0.12036562 0.13454556 0.1200161 0.12404132 0.11710835 0.29146838] mean value: 0.1453989028930664 key: score_time value: [0.01236534 0.01199627 0.01214051 0.01123166 0.01160216 0.01120901 0.01137543 0.01114058 0.01208997 0.01214266] mean value: 0.01172935962677002 key: test_mcc value: [ 0.42638684 0.52012466 0.42638684 0.17426801 0.03098437 -0.09284767 0.67846699 0.54254508 0.42560808 0.51937818] mean value: 0.3651301390500383 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.42857143 0.53333333 0.42857143 0.16666667 0.125 0. 0.66666667 0.58823529 0.42857143 0.53333333] mean value: 0.38989495798319324 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.75 0.8 0.75 0.5 0.2 0. 1. 0.71428571 0.75 0.8 ] mean value: 0.6264285714285714 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.3 0.4 0.3 0.1 0.09090909 0. 0.5 0.5 0.3 0.4 ] mean value: 0.28909090909090907 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.88405797 0.89855072 0.88405797 0.85507246 0.79710145 0.79710145 0.92647059 0.89705882 0.88235294 0.89705882] mean value: 0.8718883205456095 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.64152542 0.69152542 0.64152542 0.54152542 0.51097179 0.47413793 0.75 0.73275862 0.64137931 0.69137931] mean value: 0.6316728654162904 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.27272727 0.36363636 0.27272727 0.09090909 0.06666667 0. 0.5 0.41666667 0.27272727 0.36363636] mean value: 0.26196969696969696 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.3 MCC on Training: 0.37 Extracting tts_split_name: 80_20 Total cols in each df: CV df: 8 metaDF: 17 Adding column: Model_name Total cols in bts df: BT_df: 8 First proceeding to rowbind CV and BT dfs: Final output should have: 25 columns Combinig 2 using pd.concat by row ~ rowbind Checking Dims of df to combine: Dim of CV: (24, 8) Dim of BT: (24, 8) 8 Number of Common columns: 8 These are: ['source_data', 'Precision', 'JCC', 'MCC', 'Recall', 'Accuracy', 'F1', 'ROC_AUC'] Concatenating dfs with different resampling methods [WF]: Split type: 80_20 No. of dfs combining: 2 PASS: 2 dfs successfully combined nrows in combined_df_wf: 48 ncols in combined_df_wf: 8 PASS: proceeding to merge metadata with CV and BT dfs Adding column: Model_name ========================================================= SUCCESS: Ran multiple classifiers ======================================================= ============================================================== Running several classification models (n): 24 List of models: ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('Gaussian NB', GaussianNB()) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('K-Nearest Neighbors', KNeighborsClassifier()) ('LDA', LinearDiscriminantAnalysis()) ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(cv=3, random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Multinomial', MultinomialNB()) ('Naive Bayes', BernoulliNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=12, random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Random Forest', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42)) [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. 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Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?ð?[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.6s remaining: 1.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.6s remaining: 0.2s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.7s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.6s remaining: 1.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.6s remaining: 0.2s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.6s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.6s remaining: 1.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.6s remaining: 0.2s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.7s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.6s remaining: 1.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.6s remaining: 0.2s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.7s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.6s remaining: 1.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.6s remaining: 0.2s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.7s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.6s remaining: 1.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.6s remaining: 0.2s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.7s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.5s remaining: 1.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.6s remaining: 0.2s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.6s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42)) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=3)) ('SVC', SVC(random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=12, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ================================================================ Running classifier: 1 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.45256305 0.46772838 0.46803498 0.45768404 0.45902538 0.46337247 0.45253921 0.45535946 0.45053196 0.44827676] mean value: 0.45751156806945803 key: score_time value: [0.01633763 0.01669121 0.01643419 0.01627588 0.01666594 0.01791453 0.01625896 0.01628566 0.01612759 0.01614928] mean value: 0.016514086723327638 key: test_mcc value: [0.78077709 0.69919881 0.762589 0.74364224 0.60778029 0.5731859 0.695691 0.65809468 0.69130113 0.65517241] mean value: 0.6867432553613746 key: train_mcc value: [0.84776949 0.84589784 0.8515698 0.83256319 0.8706236 0.87060609 0.84970667 0.84044636 0.81374116 0.83671967] mean value: 0.8459643863373454 key: test_fscore value: [0.89256198 0.85483871 0.88333333 0.86956522 0.8 0.79338843 0.85483871 0.83050847 0.83928571 0.82758621] mean value: 0.8445906779061154 key: train_fscore value: [0.92409867 0.92336802 0.92585551 0.91666667 0.93499044 0.93536122 0.92511848 0.91891892 0.9073724 0.91730769] mean value: 0.9229058026302848 key: test_precision value: [0.85714286 0.8030303 0.85483871 0.87719298 0.82142857 0.77419355 0.81538462 0.83050847 0.87037037 0.82758621] mean value: 0.8331676639350197 key: train_precision value: [0.92234848 0.91902072 0.92585551 0.91320755 0.93857965 0.93358634 0.92075472 0.93150685 0.90225564 0.92801556] mean value: 0.9235131022824913 key: test_recall value: [0.93103448 0.9137931 0.9137931 0.86206897 0.77966102 0.81355932 0.89830508 0.83050847 0.81034483 0.82758621] mean value: 0.8580654587960257 key: train_recall value: [0.92585551 0.92775665 0.92585551 0.92015209 0.93142857 0.93714286 0.92952381 0.90666667 0.91254753 0.90684411] mean value: 0.9223773311606012 key: test_accuracy value: [0.88888889 0.84615385 0.88034188 0.87179487 0.8034188 0.78632479 0.84615385 0.82905983 0.84482759 0.82758621] mean value: 0.8424550545240201 key: train_accuracy value: [0.92388202 0.92293054 0.92578497 0.91627022 0.93529971 0.93529971 0.92483349 0.92007612 0.90684411 0.91825095] mean value: 0.9229471841049446 key: test_roc_auc value: [0.88924605 0.84672706 0.88062537 0.87171245 0.80362361 0.78609001 0.84570427 0.82904734 0.84482759 0.82758621] mean value: 0.8425189947399183 key: train_roc_auc value: [0.92388014 0.92292595 0.9257849 0.91626652 0.93529603 0.93530147 0.92483795 0.92006337 0.90684411 0.91825095] mean value: 0.9229451385116786 key: test_jcc value: [0.80597015 0.74647887 0.79104478 0.76923077 0.66666667 0.65753425 0.74647887 0.71014493 0.72307692 0.70588235] mean value: 0.7322508557879118 key: train_jcc value: [0.85890653 0.85764499 0.8619469 0.84615385 0.87791741 0.87857143 0.86067019 0.85 0.83044983 0.84724689] mean value: 0.8569508021532724 MCC on Blind test: 0.22 MCC on Training: 0.69 Running classifier: 2 Model_name: Bagging Classifier Model func: BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3))]) key: fit_time value: [0.88484216 0.87164235 0.92003345 0.91757083 0.86123824 0.84968257 0.88016844 0.92412925 0.89048767 0.84665036] mean value: 0.8846445322036743 key: score_time value: [0.07092619 0.05403328 0.04325199 0.05158758 0.06780791 0.08402252 0.04199672 0.06370974 0.06776166 0.07609844] mean value: 0.06211960315704346 key: test_mcc value: [0.8974284 0.84732411 0.86323787 0.79683964 0.82904734 0.74443909 0.96580947 0.84624382 0.87944107 0.91501794] mean value: 0.8584828744247343 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.94827586 0.92436975 0.93103448 0.9 0.91525424 0.87603306 0.98305085 0.92436975 0.93913043 0.95798319] mean value: 0.9299501611282828 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.94827586 0.90163934 0.93103448 0.87096774 0.91525424 0.85483871 0.98305085 0.91666667 0.94736842 0.93442623] mean value: 0.9203522542676043 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.94827586 0.94827586 0.93103448 0.93103448 0.91525424 0.89830508 0.98305085 0.93220339 0.93103448 0.98275862] mean value: 0.9401227352425481 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.94871795 0.92307692 0.93162393 0.8974359 0.91452991 0.87179487 0.98290598 0.92307692 0.93965517 0.95689655] mean value: 0.9289714117300324 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.9487142 0.92329047 0.93161894 0.89772063 0.91452367 0.87156634 0.98290473 0.92299825 0.93965517 0.95689655] mean value: 0.9289888953828171 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 5.3s remaining: 10.7s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 5.4s remaining: 10.8s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 5.4s remaining: 10.8s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 5.5s remaining: 11.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 5.5s remaining: 11.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 5.5s remaining: 11.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 5.6s remaining: 11.2s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 5.6s remaining: 11.2s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 5.6s remaining: 11.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 5.7s remaining: 1.9s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 5.8s remaining: 1.9s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 5.8s remaining: 1.9s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 5.8s remaining: 11.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 5.8s remaining: 1.9s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 5.8s remaining: 1.9s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 5.8s remaining: 1.9s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 5.8s remaining: 1.9s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 5.9s remaining: 2.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 5.9s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 5.9s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 5.9s remaining: 2.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 5.9s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 5.9s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 5.9s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 5.9s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 5.9s remaining: 2.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 5.9s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 5.9s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 6.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 5.9s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.7s remaining: 1.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.7s remaining: 0.2s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.8s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [0.90163934 0.859375 0.87096774 0.81818182 0.84375 0.77941176 0.96666667 0.859375 0.8852459 0.91935484] mean value: 0.8703968076101167 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.25 MCC on Training: 0.86 Running classifier: 3 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.11906052 0.07805181 0.09813237 0.10302377 0.08039045 0.10285926 0.09749556 0.11588573 0.11051822 0.08783913] mean value: 0.09932568073272705 key: score_time value: [0.00989985 0.00952935 0.00958681 0.00999904 0.00970888 0.00974607 0.00983691 0.00990677 0.00919533 0.00956583] mean value: 0.009697484970092773 key: test_mcc value: [0.61159649 0.71044192 0.49235618 0.71829222 0.693731 0.55552309 0.60684013 0.76449561 0.67251376 0.8104653 ] mean value: 0.6636255691382782 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.81300813 0.85714286 0.75806452 0.864 0.85245902 0.77966102 0.80672269 0.88709677 0.83478261 0.90598291] mean value: 0.8358920514643524 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.76923077 0.83606557 0.71212121 0.80597015 0.82539683 0.77966102 0.8 0.84615385 0.84210526 0.89830508] mean value: 0.8115009740779685 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.86206897 0.87931034 0.81034483 0.93103448 0.88135593 0.77966102 0.81355932 0.93220339 0.82758621 0.9137931 ] mean value: 0.8630917592051432 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8034188 0.85470085 0.74358974 0.85470085 0.84615385 0.77777778 0.8034188 0.88034188 0.8362069 0.90517241] mean value: 0.8305481874447391 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.80391584 0.85490941 0.74415546 0.85534775 0.84585038 0.77776154 0.80333139 0.8798948 0.8362069 0.90517241] mean value: 0.8306545879602572 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.68493151 0.75 0.61038961 0.76056338 0.74285714 0.63888889 0.67605634 0.79710145 0.71641791 0.828125 ] mean value: 0.7205331227017939 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.05 MCC on Training: 0.66 Running classifier: 4 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.01444387 0.01444936 0.01312494 0.01290941 0.01356888 0.01481414 0.01476669 0.01477098 0.01337671 0.01264429] mean value: 0.01388692855834961 key: score_time value: [0.01004195 0.0100174 0.00901842 0.00927877 0.01038194 0.01034451 0.01013708 0.0100925 0.00901961 0.00928307] mean value: 0.009761524200439454 key: test_mcc value: [0.67622138 0.69622043 0.59577749 0.65857939 0.69260044 0.60684013 0.69260044 0.64527829 0.74148953 0.63802587] mean value: 0.6643633397547195 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.84033613 0.85245902 0.80645161 0.83050847 0.85 0.80672269 0.85 0.832 0.87179487 0.82051282] mean value: 0.8360785619710043 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.81967213 0.8125 0.75757576 0.81666667 0.83606557 0.8 0.83606557 0.78787879 0.86440678 0.81355932] mean value: 0.8144390592504653 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.86206897 0.89655172 0.86206897 0.84482759 0.86440678 0.81355932 0.86440678 0.88135593 0.87931034 0.82758621] mean value: 0.859614260666277 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.83760684 0.84615385 0.79487179 0.82905983 0.84615385 0.8034188 0.84615385 0.82051282 0.87068966 0.81896552] mean value: 0.8313586796345417 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.83781414 0.84658095 0.79544126 0.82919345 0.84599649 0.80333139 0.84599649 0.81998831 0.87068966 0.81896552] mean value: 0.8313997662185857 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.72463768 0.74285714 0.67567568 0.71014493 0.73913043 0.67605634 0.73913043 0.71232877 0.77272727 0.69565217] mean value: 0.7188340848585459 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.13 MCC on Training: 0.66 Running classifier: 5 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.19143128 0.19385386 0.20555043 0.19600534 0.188555 0.19166136 0.18950796 0.18894219 0.18760014 0.18976045] mean value: 0.1922868013381958 key: score_time value: [0.01911831 0.0208919 0.02056742 0.01916528 0.01903105 0.01904917 0.0190022 0.01922607 0.01919031 0.01914239] mean value: 0.019438409805297853 key: test_mcc value: [0.86323787 0.79683964 0.88154465 0.88047925 0.88047925 0.86378256 0.8974284 0.88348376 0.84495318 0.87944107] mean value: 0.867166963671593 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.93103448 0.9 0.94117647 0.94017094 0.94017094 0.93103448 0.94915254 0.94308943 0.92173913 0.94017094] mean value: 0.9337739360320269 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.93103448 0.87096774 0.91803279 0.93220339 0.94827586 0.94736842 0.94915254 0.90625 0.92982456 0.93220339] mean value: 0.9265313178138355 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.93103448 0.93103448 0.96551724 0.94827586 0.93220339 0.91525424 0.94915254 0.98305085 0.9137931 0.94827586] mean value: 0.9417592051431912 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.93162393 0.8974359 0.94017094 0.94017094 0.94017094 0.93162393 0.94871795 0.94017094 0.92241379 0.93965517] mean value: 0.9332154435602712 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.93161894 0.89772063 0.94038574 0.94023963 0.94023963 0.93176505 0.9487142 0.93980129 0.92241379 0.93965517] mean value: 0.9332554061952075 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.87096774 0.81818182 0.88888889 0.88709677 0.88709677 0.87096774 0.90322581 0.89230769 0.85483871 0.88709677] mean value: 0.8760668721959044 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.11 MCC on Training: 0.87 Running classifier: 6 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [2.02145767 2.01352572 2.01694489 2.01436996 2.08877158 2.13190222 2.00502443 2.04528284 2.03891873 2.01574779] mean value: 2.0391945838928223 key: score_time value: [0.00990391 0.00938392 0.06519651 0.01021457 0.010813 0.00959277 0.00953746 0.00947428 0.00952888 0.00937176] mean value: 0.015301704406738281 key: test_mcc value: [0.8974284 0.83358601 0.83109027 0.81209819 0.74364224 0.7953815 0.88144164 0.79485681 0.82956136 0.84595998] mean value: 0.8265046401897356 key: train_mcc value: [0.98287525 0.99239535 0.992417 0.9790836 0.98287519 0.9961941 0.98478347 0.99239535 0.99051041 0.98479799] mean value: 0.9878327713920034 key: test_fscore value: [0.94827586 0.91803279 0.91666667 0.90598291 0.87394958 0.89655172 0.94214876 0.89830508 0.91071429 0.92436975] mean value: 0.9134997404263435 key: train_fscore value: [0.99143673 0.99620493 0.99618321 0.98951382 0.9914204 0.99809524 0.99236641 0.99618321 0.99523356 0.99238095] mean value: 0.9939018454253732 key: test_precision value: [0.94827586 0.875 0.88709677 0.89830508 0.86666667 0.9122807 0.91935484 0.89830508 0.94444444 0.90163934] mean value: 0.9051368801591508 key: train_precision value: [0.99238095 0.99431818 1. 0.99235182 0.99236641 0.99809524 0.99426386 0.99808795 0.99808795 0.99427481] mean value: 0.9954227180666507 key: test_recall value: [0.94827586 0.96551724 0.94827586 0.9137931 0.88135593 0.88135593 0.96610169 0.89830508 0.87931034 0.94827586] mean value: 0.9230566919929866 key: train_recall value: [0.9904943 0.99809886 0.99239544 0.98669202 0.99047619 0.99809524 0.99047619 0.99428571 0.99239544 0.9904943 ] mean value: 0.9923903675538657 key: test_accuracy value: [0.94871795 0.91452991 0.91452991 0.90598291 0.87179487 0.8974359 0.94017094 0.8974359 0.9137931 0.92241379] mean value: 0.9126805187150016 key: train_accuracy value: [0.99143673 0.9961941 0.9961941 0.98953378 0.99143673 0.99809705 0.9923882 0.9961941 0.99524715 0.99239544] mean value: 0.9939117371469506 key: test_roc_auc value: [0.9487142 0.91496201 0.9148159 0.90604909 0.87171245 0.89757452 0.9399474 0.8974284 0.9137931 0.92241379] mean value: 0.9127410870835769 key: train_roc_auc value: [0.99143762 0.99619229 0.99619772 0.98953648 0.99143581 0.99809705 0.99238638 0.99619229 0.99524715 0.99239544] mean value: 0.9939118232844469 key: test_jcc value: [0.90163934 0.84848485 0.84615385 0.828125 0.7761194 0.8125 0.890625 0.81538462 0.83606557 0.859375 ] mean value: 0.8414472631041171 key: train_jcc value: [0.98301887 0.99243856 0.99239544 0.97924528 0.98298677 0.99619772 0.98484848 0.99239544 0.99051233 0.98487713] mean value: 0.9878916020380538 MCC on Blind test: 0.3 MCC on Training: 0.83 Running classifier: 7 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.01180053 0.01215529 0.01219988 0.01215124 0.01211524 0.01197052 0.01211786 0.01223516 0.01219416 0.01269102] mean value: 0.012163090705871581 key: score_time value: [0.00911641 0.00930476 0.00931454 0.0094099 0.00946569 0.009408 0.00936222 0.00937033 0.00925183 0.01032519] mean value: 0.00943288803100586 key: test_mcc value: [0.26906423 0.48308598 0.35656112 0.35912786 0.30054058 0.38749975 0.55139163 0.43618334 0.51847585 0.35143029] mean value: 0.4013360632805054 key: train_mcc value: [0.44377167 0.43396751 0.43076187 0.43814122 0.43687728 0.43897707 0.41782403 0.42913098 0.44473204 0.43909531] mean value: 0.43532789783195824 key: test_fscore value: [0.656 0.75968992 0.6984127 0.703125 0.672 0.71428571 0.79389313 0.72727273 0.76666667 0.71532847] mean value: 0.7206674326042704 key: train_fscore value: [0.73655914 0.7371179 0.73415493 0.73931997 0.73867596 0.73977372 0.73043478 0.73519164 0.74216028 0.73767606] mean value: 0.7371064368205164 key: test_precision value: [0.6119403 0.69014085 0.64705882 0.64285714 0.63636364 0.67164179 0.72222222 0.70967742 0.74193548 0.62025316] mean value: 0.6694090827377843 key: train_precision value: [0.69661017 0.68174475 0.68360656 0.68276973 0.68057785 0.68108974 0.672 0.67736758 0.68488746 0.68688525] mean value: 0.6827539077372298 key: test_recall value: [0.70689655 0.84482759 0.75862069 0.77586207 0.71186441 0.76271186 0.88135593 0.74576271 0.79310345 0.84482759] mean value: 0.7825832846288721 key: train_recall value: [0.78136882 0.80228137 0.79277567 0.80608365 0.80761905 0.80952381 0.8 0.80380952 0.80988593 0.79657795] mean value: 0.8009925764982799 key: test_accuracy value: [0.63247863 0.73504274 0.67521368 0.67521368 0.64957265 0.69230769 0.76923077 0.71794872 0.75862069 0.6637931 ] mean value: 0.6969422340111995 key: train_accuracy value: [0.72026641 0.71360609 0.71265461 0.71550904 0.71455756 0.71550904 0.70504282 0.71075167 0.71863118 0.71673004] mean value: 0.7143258457453159 key: test_roc_auc value: [0.63310929 0.73597312 0.67592051 0.67606663 0.64903565 0.69170076 0.76826417 0.71770894 0.75862069 0.6637931 ] mean value: 0.6970192869666862 key: train_roc_auc value: [0.72020822 0.71352164 0.71257831 0.71542278 0.71464603 0.71559841 0.70513308 0.71084012 0.71863118 0.71673004] mean value: 0.714330979540105 key: test_jcc value: [0.48809524 0.6125 0.53658537 0.54216867 0.5060241 0.55555556 0.65822785 0.57142857 0.62162162 0.55681818] mean value: 0.564902515355843 key: train_jcc value: [0.58297872 0.58367911 0.57997218 0.58644537 0.58563536 0.58701657 0.57534247 0.58126722 0.5900277 0.58437936] mean value: 0.583674406467527 MCC on Blind test: 0.14 MCC on Training: 0.4 Running classifier: 8 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.6841507 0.7489059 0.56479645 0.52992487 0.55688977 0.56079793 0.53397107 0.51126003 0.63971496 0.52933764] mean value: 0.585974931716919 key: score_time value: [0.04368901 0.04341578 0.0430367 0.0448215 0.04617906 0.02678227 0.036834 0.04558516 0.0487628 0.04308486] mean value: 0.042219114303588864 key: test_mcc value: [0.783608 0.71829222 0.75284822 0.73056182 0.77790766 0.73009331 0.693731 0.71756445 0.69006556 0.7787612 ] mean value: 0.7373433434590014 key: train_mcc value: [0.91246424 0.92388836 0.91246424 0.91627648 0.91627014 0.92198081 0.9181749 0.91246424 0.91254753 0.91635643] mean value: 0.9162887379177045 key: test_fscore value: [0.89430894 0.864 0.88 0.86885246 0.88888889 0.87096774 0.85245902 0.86614173 0.84210526 0.88288288] mean value: 0.8710606927647883 key: train_fscore value: [0.95627376 0.96204934 0.95627376 0.95825427 0.95809524 0.96098953 0.95908658 0.95619048 0.95627376 0.95809524] mean value: 0.9581581968452584 key: test_precision value: [0.84615385 0.80597015 0.82089552 0.828125 0.89655172 0.83076923 0.82539683 0.80882353 0.85714286 0.9245283 ] mean value: 0.8444356986541038 key: train_precision value: [0.95627376 0.96022727 0.95627376 0.95643939 0.95809524 0.96007605 0.9581749 0.95619048 0.95627376 0.95992366] mean value: 0.9577948288420526 key: test_recall value: [0.94827586 0.93103448 0.94827586 0.9137931 0.88135593 0.91525424 0.88135593 0.93220339 0.82758621 0.84482759] mean value: 0.90239625949737 key: train_recall value: [0.95627376 0.96387833 0.95627376 0.96007605 0.95809524 0.96190476 0.96 0.95619048 0.95627376 0.95627376] mean value: 0.9585239905848271 key: test_accuracy value: [0.88888889 0.85470085 0.87179487 0.86324786 0.88888889 0.86324786 0.84615385 0.85470085 0.84482759 0.88793103] mean value: 0.8664382552313586 key: train_accuracy value: [0.95623216 0.96194101 0.95623216 0.95813511 0.95813511 0.96098953 0.95908658 0.95623216 0.95627376 0.9581749 ] mean value: 0.958143249413016 key: test_roc_auc value: [0.88939217 0.85534775 0.87244302 0.86367621 0.88895383 0.86279953 0.84585038 0.85403273 0.84482759 0.88793103] mean value: 0.8665254237288135 key: train_roc_auc value: [0.95623212 0.96193916 0.95623212 0.95813326 0.95813507 0.9609904 0.95908745 0.95623212 0.95627376 0.9581749 ] mean value: 0.9581430382038747 key: test_jcc value: [0.80882353 0.76056338 0.78571429 0.76811594 0.8 0.77142857 0.74285714 0.76388889 0.72727273 0.79032258] mean value: 0.7718987048529218 key: train_jcc value: [0.91621129 0.92687386 0.91621129 0.91985428 0.91956124 0.92490842 0.9213894 0.91605839 0.91621129 0.91956124] mean value: 0.9196840719762642 MCC on Blind test: 0.11 MCC on Training: 0.74 Running classifier: 9 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.01472497 0.01108932 0.01202416 0.01264358 0.01294017 0.01284313 0.01214004 0.01124835 0.01203895 0.01332355] mean value: 0.01250162124633789 key: score_time value: [0.04292297 0.01483011 0.01976538 0.01710606 0.01646113 0.01880455 0.0220108 0.0158112 0.01654696 0.02192855] mean value: 0.020618772506713866 key: test_mcc value: [0.73367258 0.71829222 0.64918022 0.71219532 0.69228521 0.6087526 0.61722307 0.69194244 0.72413793 0.69006556] mean value: 0.6837747154961579 key: train_mcc value: [0.77344827 0.77907071 0.79332996 0.75658872 0.76746554 0.76711816 0.78271835 0.77430015 0.77007301 0.76586018] mean value: 0.7729973044856581 key: test_fscore value: [0.87096774 0.864 0.832 0.85950413 0.84745763 0.81300813 0.82170543 0.85496183 0.86206897 0.84210526] mean value: 0.8467779118459628 key: train_fscore value: [0.88970588 0.89236431 0.89860465 0.88175985 0.88642659 0.88600556 0.89381348 0.88909599 0.88807339 0.88581952] mean value: 0.8891669235080626 key: test_precision value: [0.81818182 0.80597015 0.7761194 0.82539683 0.84745763 0.78125 0.75714286 0.77777778 0.86206897 0.85714286] mean value: 0.8108508280516826 key: train_precision value: [0.86120996 0.86452763 0.87978142 0.85132743 0.86021505 0.86281588 0.86738351 0.87043796 0.85815603 0.85892857] mean value: 0.8634783454826191 key: test_recall value: [0.93103448 0.93103448 0.89655172 0.89655172 0.84745763 0.84745763 0.89830508 0.94915254 0.86206897 0.82758621] mean value: 0.8887200467562829 key: train_recall value: [0.92015209 0.92205323 0.91825095 0.91444867 0.91428571 0.91047619 0.92190476 0.90857143 0.92015209 0.91444867] mean value: 0.9164743798660148 key: test_accuracy value: [0.86324786 0.85470085 0.82051282 0.85470085 0.84615385 0.8034188 0.8034188 0.83760684 0.86206897 0.84482759] mean value: 0.839065723548482 key: train_accuracy value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( [0.88582303 0.88867745 0.89628925 0.87725975 0.8829686 0.8829686 0.8905804 0.8867745 0.88403042 0.88212928] mean value: 0.8857501275265636 key: test_roc_auc value: [0.86382233 0.85534775 0.82115722 0.85505552 0.84614261 0.80303916 0.80260082 0.83664524 0.86206897 0.84482759] mean value: 0.839070718877849 key: train_roc_auc value: [0.88579033 0.88864566 0.89626833 0.87722433 0.88299837 0.88299475 0.89061018 0.88679522 0.88403042 0.88212928] mean value: 0.8857486873076226 key: test_jcc value: [0.77142857 0.76056338 0.71232877 0.75362319 0.73529412 0.68493151 0.69736842 0.74666667 0.75757576 0.72727273] mean value: 0.7347053104303504 key: train_jcc value: [0.8013245 0.80564784 0.81587838 0.78852459 0.7960199 0.7953411 0.80801336 0.80033557 0.79867987 0.79504132] mean value: 0.8004806427415663 MCC on Blind test: 0.12 MCC on Training: 0.68 Running classifier: 10 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.0613997 0.09296608 0.06733203 0.06301284 0.08753657 0.07435369 0.06292319 0.08201265 0.06213021 0.0700531 ] mean value: 0.07237200736999512 key: score_time value: [0.01853323 0.01973581 0.01271296 0.01741147 0.02481294 0.01258731 0.01254106 0.01255727 0.01910901 0.01244783] mean value: 0.016244888305664062 key: test_mcc value: [0.64168717 0.69408772 0.79924461 0.74611818 0.73056182 0.69622043 0.76104153 0.67975207 0.62217102 0.77690202] mean value: 0.7147786568626666 key: train_mcc value: [0.80026365 0.81164286 0.79453528 0.78687438 0.80020207 0.79448126 0.7925901 0.81553033 0.82706944 0.79482046] mean value: 0.8018009826103448 key: test_fscore value: [0.81415929 0.85 0.90163934 0.86486486 0.85714286 0.83928571 0.88333333 0.848 0.80357143 0.8907563 ] mean value: 0.85527531370169 key: train_fscore value: [0.89952153 0.90633869 0.89674952 0.89373814 0.89971347 0.89714286 0.89589303 0.90682037 0.91406988 0.89635317] mean value: 0.9006340649077249 key: test_precision value: [0.83636364 0.82258065 0.859375 0.90566038 0.90566038 0.88679245 0.86885246 0.8030303 0.83333333 0.86885246] mean value: 0.8590501043468519 key: train_precision value: [0.90558767 0.90207156 0.90192308 0.89204545 0.90229885 0.89714286 0.89846743 0.91472868 0.90806754 0.90503876] mean value: 0.9027371887892602 key: test_recall value: [0.79310345 0.87931034 0.94827586 0.82758621 0.81355932 0.79661017 0.89830508 0.89830508 0.77586207 0.9137931 ] mean value: 0.8544710695499708 key: train_recall value: [0.89353612 0.91064639 0.89163498 0.89543726 0.89714286 0.89714286 0.89333333 0.89904762 0.92015209 0.8878327 ] mean value: 0.8985906210392901 key: test_accuracy value: [0.82051282 0.84615385 0.8974359 0.87179487 0.86324786 0.84615385 0.88034188 0.83760684 0.81034483 0.88793103] mean value: 0.8561523725316829 key: train_accuracy value: [0.90009515 0.905804 0.89724072 0.89343482 0.90009515 0.89724072 0.89628925 0.90770695 0.9134981 0.8973384 ] mean value: 0.900874325737212 key: test_roc_auc value: [0.82028054 0.84643483 0.89786674 0.87142022 0.86367621 0.84658095 0.88018703 0.83708358 0.81034483 0.88793103] mean value: 0.8561805961426068 key: train_roc_auc value: [0.90010139 0.90579938 0.89724606 0.89343292 0.90009234 0.89724063 0.89628644 0.90769871 0.9134981 0.8973384 ] mean value: 0.9008734383487236 key: test_jcc value: [0.68656716 0.73913043 0.82089552 0.76190476 0.75 0.72307692 0.79104478 0.73611111 0.67164179 0.8030303 ] mean value: 0.7483402787637051 key: train_jcc value: [0.8173913 0.82871972 0.81282496 0.80789022 0.81770833 0.8134715 0.81141869 0.82952548 0.84173913 0.81217391] mean value: 0.819286325501564 MCC on Blind test: 0.18 MCC on Training: 0.71 Running classifier: 11 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.0470202 0.06375337 0.07892323 0.07455873 0.04706264 0.04708099 0.04675555 0.04800558 0.04674864 0.04970717] mean value: 0.05496160984039307 key: score_time value: [0.01122093 0.01794338 0.01250601 0.0125711 0.01238823 0.01240993 0.01234579 0.01238918 0.01241732 0.0122726 ] mean value: 0.01284644603729248 key: test_mcc value: [0.69277992 0.65807378 0.61843713 0.63025127 0.65857939 0.53843908 0.67524119 0.56318771 0.53456222 0.68065833] mean value: 0.6250210008795544 key: train_mcc value: [0.69847937 0.70659302 0.68309392 0.69151459 0.70002634 0.7182616 0.68158744 0.70966509 0.69147624 0.67751169] mean value: 0.6958209305909577 key: test_fscore value: [0.84745763 0.8372093 0.81889764 0.82258065 0.82758621 0.77310924 0.84033613 0.796875 0.76521739 0.848 ] mean value: 0.8177269188752951 key: train_fscore value: [0.85291397 0.8574057 0.84522706 0.8503214 0.85479452 0.86292548 0.84473198 0.85767442 0.84976959 0.84200743] mean value: 0.851777155033979 key: test_precision value: [0.83333333 0.76056338 0.75362319 0.77272727 0.84210526 0.76666667 0.83333333 0.73913043 0.77192982 0.79104478] mean value: 0.7864457473369403 key: train_precision value: [0.83063063 0.83065954 0.82459313 0.82238011 0.82105263 0.83451957 0.82046679 0.83818182 0.82468694 0.82363636] mean value: 0.8270807515807406 key: test_recall value: [0.86206897 0.93103448 0.89655172 0.87931034 0.81355932 0.77966102 0.84745763 0.86440678 0.75862069 0.9137931 ] mean value: 0.854646405610754 key: train_recall value: [0.87642586 0.88593156 0.86692015 0.88022814 0.89142857 0.89333333 0.87047619 0.87809524 0.87642586 0.86121673] mean value: 0.8780481622306718 key: test_accuracy value: [0.84615385 0.82051282 0.8034188 0.81196581 0.82905983 0.76923077 0.83760684 0.77777778 0.76724138 0.8362069 ] mean value: 0.8099174771588565 key: train_accuracy value: [0.84871551 0.85252141 0.84110371 0.84490961 0.84871551 0.85823026 0.84015224 0.85442436 0.84505703 0.83840304] mean value: 0.8472232673571793 key: test_roc_auc value: [0.84628872 0.82144944 0.80420807 0.81253653 0.82919345 0.76914085 0.83752192 0.77703098 0.76724138 0.8362069 ] mean value: 0.8100818234950321 key: train_roc_auc value: [0.84868912 0.85248959 0.84107912 0.84487597 0.84875611 0.85826362 0.84018106 0.85444686 0.84505703 0.83840304] mean value: 0.8472241535397428 /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( key: test_jcc value: [0.73529412 0.72 0.69333333 0.69863014 0.70588235 0.63013699 0.72463768 0.66233766 0.61971831 0.73611111] mean value: 0.692608169167659 key: train_jcc value: [0.74354839 0.75040258 0.73194222 0.73961661 0.74641148 0.75889968 0.7312 0.75081433 0.73878205 0.72712681] mean value: 0.7418744141030211 MCC on Blind test: 0.32 MCC on Training: 0.63 Running classifier: 12 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(cv=3, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(cv=3, random_state=42))]) key: fit_time value: [0.61477399 0.72018003 0.62067771 0.66625023 0.62600183 0.77957106 0.6391716 0.62683487 0.82979512 0.87841868] mean value: 0.7001675128936767 key: score_time value: [0.01243401 0.01264715 0.0124886 0.01235509 0.01237226 0.01240802 0.01242137 0.01241755 0.01233339 0.02024961] mean value: 0.0132127046585083 key: test_mcc value: [0.65857939 0.66187903 0.76118097 0.76066628 0.69408772 0.65809468 0.76104153 0.66394775 0.65673607 0.71210181] mean value: 0.6988315238389199 key: train_mcc value: [0.82336582 0.79655469 0.80999718 0.80975972 0.79448776 0.81356583 0.78503888 0.83836947 0.79661394 0.80039325] mean value: 0.806814653534669 key: test_fscore value: [0.83050847 0.83606557 0.88135593 0.87931034 0.84210526 0.83050847 0.88333333 0.84126984 0.82142857 0.86178862] mean value: 0.850767442702983 key: train_fscore value: [0.91031823 0.89934149 0.90366089 0.90439771 0.8973384 0.90719697 0.89309366 0.91834774 0.8987701 0.90047393] mean value: 0.9032939118192074 key: test_precision value: [0.81666667 0.796875 0.86666667 0.87931034 0.87272727 0.83050847 0.86885246 0.79104478 0.85185185 0.81538462] mean value: 0.8389888127836727 key: train_precision value: [0.92367906 0.89013035 0.91601562 0.90961538 0.89563567 0.90207156 0.88721805 0.92635659 0.89453861 0.8979206 ] mean value: 0.9043181506389069 key: test_recall value: [0.84482759 0.87931034 0.89655172 0.87931034 0.81355932 0.83050847 0.89830508 0.89830508 0.79310345 0.9137931 ] mean value: 0.8647574517825835 key: train_recall value: [0.8973384 0.90874525 0.89163498 0.89923954 0.89904762 0.91238095 0.89904762 0.91047619 0.90304183 0.90304183] mean value: 0.9023994206047437 key: test_accuracy value: [0.82905983 0.82905983 0.88034188 0.88034188 0.84615385 0.82905983 0.88034188 0.82905983 0.82758621 0.85344828] mean value: 0.8484453286177425 key: train_accuracy value: [0.91151284 0.8981922 0.90485252 0.90485252 0.89724072 0.90675547 0.89248335 0.91912464 0.89828897 0.90019011] mean value: 0.9033493359574261 key: test_roc_auc value: [0.82919345 0.82948568 0.88047925 0.88033314 0.84643483 0.82904734 0.88018703 0.82846289 0.82758621 0.85344828] mean value: 0.8484658094681473 key: train_roc_auc value: [0.91152634 0.89818215 0.90486511 0.90485787 0.89724244 0.90676082 0.89248959 0.91911642 0.89828897 0.90019011] mean value: 0.9033519826181425 key: test_jcc value: [0.71014493 0.71830986 0.78787879 0.78461538 0.72727273 0.71014493 0.79104478 0.7260274 0.6969697 0.75714286] mean value: 0.7409551341486524 key: train_jcc value: [0.83539823 0.81709402 0.82425308 0.82547993 0.8137931 0.83015598 0.80683761 0.84902309 0.8161512 0.81896552] mean value: 0.8237151753010984 MCC on Blind test: 0.1 MCC on Training: 0.7 Running classifier: 13 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [3.95082617 2.1954906 4.42047286 2.58619571 3.66827202 2.25284553 4.46788454 4.14391613 2.15071225 2.35763478] mean value: 3.2194250583648683 key: score_time value: [0.01472569 0.01652813 0.01460767 0.01331425 0.01381421 0.01375723 0.01388621 0.01373529 0.01354122 0.01296568] mean value: 0.014087557792663574 key: test_mcc value: [0.77888301 0.76490322 0.85651622 0.76104153 0.84628872 0.69228521 0.81209819 0.81480097 0.69006556 0.78157516] mean value: 0.7798457793067903 key: train_mcc value: [0.97154229 0.91436869 0.95631642 0.91524774 0.94923925 0.9036677 0.97722804 0.96385944 0.91635643 0.88444029] mean value: 0.9352266277209751 key: test_fscore value: [0.8907563 0.8852459 0.928 0.87719298 0.92307692 0.84745763 0.90598291 0.91056911 0.84210526 0.89430894] mean value: 0.8904695954733348 key: train_fscore value: [0.98564593 0.95726496 0.9782814 0.95626822 0.97381183 0.95043732 0.98850575 0.98185291 0.95825427 0.94311927] mean value: 0.9673441856070646 key: test_precision value: [0.86885246 0.84375 0.86567164 0.89285714 0.93103448 0.84745763 0.9137931 0.875 0.85714286 0.84615385] mean value: 0.8741713160286825 key: train_precision value: [0.99229287 0.95635674 0.97185741 0.97813121 0.99209486 0.9702381 0.99421965 0.98467433 0.95643939 0.91134752] mean value: 0.9707652082003102 key: test_recall value: [0.9137931 0.93103448 1. 0.86206897 0.91525424 0.84745763 0.89830508 0.94915254 0.82758621 0.94827586] mean value: 0.9092928112215081 key: train_recall value: [0.97908745 0.9581749 0.98479087 0.93536122 0.95619048 0.93142857 0.98285714 0.97904762 0.96007605 0.97718631] mean value: 0.964420061560746 key: test_accuracy value: [0.88888889 0.88034188 0.92307692 0.88034188 0.92307692 0.84615385 0.90598291 0.90598291 0.84482759 0.88793103] mean value: 0.8886604774535808 key: train_accuracy value: [0.98572788 0.95718363 0.97811608 0.95718363 0.97431018 0.95147479 0.9885823 0.98192198 0.9581749 0.94106464] mean value: 0.9673740019463629 key: test_roc_auc value: [0.88909994 0.88077148 0.92372881 0.88018703 0.92314436 0.84614261 0.90604909 0.90561075 0.84482759 0.88793103] mean value: 0.8887492694330799 key: train_roc_auc value: [0.9857342 0.95718269 0.97810972 0.95720442 0.97429296 0.95145573 0.98857686 0.98191925 0.9581749 0.94106464] mean value: 0.9673715372080391 key: test_jcc value: [0.8030303 0.79411765 0.86567164 0.78125 0.85714286 0.73529412 0.828125 0.8358209 0.72727273 0.80882353] mean value: 0.8036548718876968 key: train_jcc value: [0.97169811 0.91803279 0.95748614 0.91620112 0.9489603 0.90555556 0.97727273 0.96435272 0.91985428 0.89236111] mean value: 0.9371774851552124 MCC on Blind test: 0.16 MCC on Training: 0.78 Running classifier: 14 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.01639676 0.01631832 0.01608038 0.01647258 0.01626253 0.01618028 0.01659441 0.01627398 0.01633143 0.01630282] mean value: 0.016321349143981933 key: score_time value: [0.01238203 0.01252103 0.01253772 0.01251602 0.01231265 0.01235342 0.01230669 0.01237822 0.01237583 0.01218152] mean value: 0.012386512756347657 key: test_mcc value: [0.16981679 0.34309574 0.29191322 0.21796427 0.38511303 0.2660668 0.29918079 0.45892669 0.32763491 0.29852138] mean value: 0.305823361986921 key: train_mcc value: [0.27761971 0.37457232 0.33639455 0.3125063 0.33339174 0.31802524 0.3244295 0.31471404 0.33814905 0.33979013] mean value: 0.32695925680167554 key: test_fscore value: [0.6259542 0.69767442 0.67692308 0.63492063 0.70491803 0.656 0.66115702 0.75 0.66666667 0.67716535] mean value: 0.6751379407499295 key: train_fscore value: [0.66724739 0.7037702 0.68637993 0.67847653 0.68627451 0.67914439 0.68379097 0.67907801 0.68971631 0.68977778] mean value: 0.6843656004768054 key: test_precision value: [0.56164384 0.63380282 0.61111111 0.58823529 0.68253968 0.62121212 0.64516129 0.69565217 0.66101695 0.62318841] mean value: 0.6323563680683677 key: train_precision value: [0.61575563 0.66666667 0.64915254 0.63515755 0.64489112 0.63819095 0.63907285 0.63515755 0.6461794 0.64774624] mean value: 0.6417970497726774 key: test_recall value: [0.70689655 0.77586207 0.75862069 0.68965517 0.72881356 0.69491525 0.6779661 0.81355932 0.67241379 0.74137931] mean value: 0.7260081823495033 key: train_recall value: [0.72813688 0.74524715 0.72813688 0.72813688 0.73333333 0.72571429 0.7352381 0.72952381 0.73954373 0.73764259] mean value: 0.7330653630273402 key: test_accuracy value: [0.58119658 0.66666667 0.64102564 0.60683761 0.69230769 0.63247863 0.64957265 0.72649573 0.6637931 0.64655172] mean value: 0.6506926024167404 key: train_accuracy value: [0.63653663 0.68601332 0.66698382 0.65461465 0.66508088 0.65746908 0.6603235 0.65556613 0.66730038 0.66825095] mean value: 0.6618139342216176 key: test_roc_auc value: [0.58226184 0.66759205 0.64202221 0.60753945 0.69199299 0.63194039 0.64932788 0.72574518 0.6637931 0.64655172] mean value: 0.6508766803039159 key: train_roc_auc value: [0.63644939 0.68595691 0.66692558 0.65454463 0.66514575 0.65753395 0.66039471 0.65563643 0.66730038 0.66825095] mean value: 0.6618138692739454 key: test_jcc value: [0.45555556 0.53571429 0.51162791 0.46511628 0.5443038 0.48809524 0.49382716 0.6 0.5 0.51190476] mean value: 0.5106144985278535 key: train_jcc value: [0.50065359 0.54293629 0.52251023 0.51340483 0.52238806 0.51417004 0.51951548 0.51409396 0.52638701 0.52645862] mean value: 0.5202518103715466 MCC on Blind test: 0.13 MCC on Training: 0.31 Running classifier: 15 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.01661491 0.01673579 0.01683807 0.01683974 0.01673079 0.01664281 0.01693773 0.01664925 0.01668119 0.01682138] mean value: 0.01674916744232178 key: score_time value: [0.01241469 0.01247334 0.01235604 0.01240969 0.01240444 0.01281619 0.01240015 0.01241803 0.01239896 0.01240063] mean value: 0.012449216842651368 key: test_mcc value: [0.45214097 0.43246402 0.51417612 0.54908751 0.47501997 0.38607028 0.60657471 0.4968457 0.45818979 0.50783338] mean value: 0.4878402453245253 key: train_mcc value: [0.52095909 0.51712221 0.51842636 0.49579783 0.52023554 0.52801179 0.49905644 0.51331712 0.51976651 0.50762937] mean value: 0.5140322267621686 key: test_fscore value: [0.7480916 0.73846154 0.77165354 0.79104478 0.75590551 0.70967742 0.81818182 0.76923077 0.75 0.7761194 ] mean value: 0.7628366382504987 key: train_fscore value: [0.77748918 0.77749361 0.77604167 0.76738197 0.77720207 0.7806563 0.76790336 0.77452668 0.77681661 0.77214101] mean value: 0.7747652467778952 key: test_precision value: [0.67123288 0.66666667 0.71014493 0.69736842 0.70588235 0.67692308 0.73972603 0.70422535 0.68571429 0.68421053] mean value: 0.6942094513372123 key: train_precision value: [0.71383148 0.70479134 0.71405751 0.69953052 0.71090047 0.71406003 0.70189274 0.70643642 0.71269841 0.70486656] mean value: 0.7083065493062892 key: test_recall value: [0.84482759 0.82758621 0.84482759 0.9137931 0.81355932 0.74576271 0.91525424 0.84745763 0.82758621 0.89655172] mean value: 0.8477206312098188 key: train_recall value: [0.85361217 0.86692015 0.84980989 0.84980989 0.85714286 0.86095238 0.84761905 0.85714286 0.85361217 0.85361217] mean value: 0.8550233568712656 key: test_accuracy value: [0.71794872 0.70940171 0.75213675 0.76068376 0.73504274 0.69230769 0.79487179 0.74358974 0.72413793 0.74137931] mean value: 0.7371500147362217 key: train_accuracy value: [0.75547098 0.75166508 0.75451951 0.74215033 0.75451951 0.7583254 0.74405328 0.75071361 0.75475285 0.74809886] mean value: 0.7514269408457633 key: test_roc_auc value: [0.71902396 0.71040327 0.75292227 0.7619813 0.73436587 0.69184687 0.79383402 0.74269433 0.72413793 0.74137931] mean value: 0.7372589129164231 key: train_roc_auc value: [0.75537751 0.75155531 0.75442875 0.7420478 0.75461706 0.75842296 0.74415173 0.75081477 0.75475285 0.74809886] mean value: 0.7514267608183958 key: test_jcc value: [0.59756098 0.58536585 0.62820513 0.65432099 0.60759494 0.55 0.69230769 0.625 0.6 0.63414634] mean value: 0.6174501915607709 key: train_jcc value: [0.63597734 0.63598326 0.63404255 0.62256267 0.63559322 0.64022663 0.6232493 0.63202247 0.63507779 0.62885154] mean value: 0.6323586782969054 MCC on Blind test: 0.03 MCC on Training: 0.49 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=12, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.04733777 0.03294945 0.0484643 0.02867508 0.03450418 0.06969976 0.03408003 0.04488969 0.03902698 0.03136516] mean value: 0.04109923839569092 key: score_time value: [0.0114789 0.01241684 0.01233768 0.01237631 0.01790905 0.01118851 0.01199126 0.01205826 0.02006364 0.01610351] mean value: 0.01379239559173584 key: test_mcc value: [0.55654161 0.54547226 0.74648621 0.57355974 0.1330081 0.5924841 0.68373622 0.49889827 0.50067019 0.70490738] mean value: 0.5535764073152446 key: train_mcc value: [0.58082847 0.67736191 0.69543276 0.64061299 0.16829078 0.71855198 0.67980489 0.56138221 0.62814426 0.64711079] mean value: 0.599752104341658 key: test_fscore value: [0.79166667 0.78873239 0.87603306 0.78991597 0.67816092 0.80645161 0.82568807 0.77124183 0.75630252 0.859375 ] mean value: 0.7943568042182372 key: train_fscore value: [0.80372382 0.84705882 0.83767535 0.81774349 0.67873303 0.86481647 0.82965932 0.7962675 0.81818182 0.83390411] mean value: 0.8127763728871142 key: test_precision value: [0.6627907 0.66666667 0.84126984 0.7704918 0.51304348 0.76923077 0.9 0.62765957 0.73770492 0.78571429] mean value: 0.7274572034596412 key: train_precision value: [0.67889908 0.75903614 0.88559322 0.8297456 0.51369863 0.81587838 0.87526427 0.67279895 0.79891304 0.75856698] mean value: 0.758839429390651 key: test_recall value: [0.98275862 0.96551724 0.9137931 0.81034483 1. 0.84745763 0.76271186 1. 0.77586207 0.94827586] mean value: 0.9006721215663356 key: train_recall value: [0.98479087 0.9581749 0.79467681 0.80608365 1. 0.92 0.78857143 0.9752381 0.83840304 0.92585551] mean value: 0.899179431468405 key: test_accuracy value: [0.74358974 0.74358974 0.87179487 0.78632479 0.52136752 0.79487179 0.83760684 0.7008547 0.75 0.84482759] mean value: 0.7594827586206897 key: train_accuracy value: [0.75927688 0.82683159 0.84586108 0.82017127 0.52711703 0.85632731 0.83824929 0.75071361 0.81368821 0.81558935] mean value: 0.7853825616016612 key: test_roc_auc value: [0.7456166 0.74547049 0.87215079 0.78652835 0.51724138 0.79441847 0.83825248 0.69827586 0.75 0.84482759] mean value: 0.7592781998831093 key: train_roc_auc value: [0.7590621 0.8267065 0.84590983 0.82018468 0.52756654 0.85638783 0.83820206 0.75092703 0.81368821 0.81558935] mean value: 0.7854224153539742 key: test_jcc value: [0.65517241 0.65116279 0.77941176 0.65277778 0.51304348 0.67567568 0.703125 0.62765957 0.60810811 0.75342466] mean value: 0.6619561241021422 key: train_jcc value: [0.67185473 0.73469388 0.72068966 0.69168026 0.51369863 0.76182965 0.70890411 0.66149871 0.69230769 0.71512482] mean value: 0.6872282137333701 MCC on Blind test: 0.19 MCC on Training: 0.55 Running classifier: 17 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.04196882 0.04159331 0.04176879 0.04147792 0.04247665 0.04183125 0.04154062 0.04166889 0.04171848 0.04158854] mean value: 0.04176332950592041 key: score_time value: [0.01349235 0.01372933 0.0132339 0.01334286 0.01333833 0.01358271 0.01316762 0.01339507 0.01328874 0.0133214 ] mean value: 0.013389229774475098 key: test_mcc value: [0.6609928 0.6609928 0.70108874 0.49044237 0.65892973 0.63535084 0.6188388 0.6995041 0.64392092 0.67082039] mean value: 0.6440881501669768 key: train_mcc value: [0.66226152 0.66672815 0.63558814 0.64297774 0.64027915 0.67439778 0.68786049 0.63585383 0.66266469 0.64193499] mean value: 0.6550546478353287 key: test_fscore value: [0.83453237 0.83453237 0.85294118 0.76315789 0.83687943 0.82857143 0.81944444 0.85507246 0.82857143 0.84057971] mean value: 0.8294282727533326 key: train_fscore value: [0.83691329 0.83891547 0.82509804 0.82834646 0.82677165 0.84202085 0.84814216 0.82482325 0.83691329 0.82769473] mean value: 0.833563918669004 key: test_precision value: [0.71604938 0.71604938 0.74358974 0.61702128 0.7195122 0.71604938 0.69411765 0.74683544 0.70731707 0.725 ] mean value: 0.7101541526723116 key: train_precision value: [0.71956224 0.72252747 0.70226969 0.70698925 0.70469799 0.72714681 0.73632539 0.70187166 0.71956224 0.70604027] mean value: 0.7146993012653552 key: test_recall value: [1. 1. 1. 1. 1. 0.98305085 1. 1. 1. 1. ] mean value: 0.9983050847457626 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8034188 0.8034188 0.82905983 0.69230769 0.8034188 0.79487179 0.77777778 0.82905983 0.79310345 0.81034483] mean value: 0.7936781609195404 key: train_accuracy value: [0.80494767 0.80780209 0.78782112 0.7925785 0.79067555 0.81255947 0.82112274 0.78782112 0.80513308 0.7918251 ] mean value: 0.8002286433706084 key: test_roc_auc value: [0.80508475 0.80508475 0.83050847 0.69491525 0.80172414 0.79324956 0.77586207 0.82758621 0.79310345 0.81034483] mean value: 0.7937463471654004 key: train_roc_auc value: [0.8047619 0.80761905 0.78761905 0.79238095 0.79087452 0.81273764 0.82129278 0.78802281 0.80513308 0.7918251 ] mean value: 0.8002266883939887 key: test_jcc value: [0.71604938 0.71604938 0.74358974 0.61702128 0.7195122 0.70731707 0.69411765 0.74683544 0.70731707 0.725 ] mean value: 0.7092809217177799 key: train_jcc value: [0.71956224 0.72252747 0.70226969 0.70698925 0.70469799 0.72714681 0.73632539 0.70187166 0.71956224 0.70604027] mean value: 0.7146993012653552 MCC on Blind test: -0.1 MCC on Training: 0.64 Running classifier: 18 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42))]) key: fit_time value: [0.96988821 1.00151849 0.99621558 0.95765352 0.97107482 0.94466114 0.99163961 1.09072351 0.99155331 0.91796041] mean value: 0.9832888603210449 key: score_time value: [0.24089742 0.15402293 0.15801692 0.17539454 0.13835382 0.15013671 0.17070603 0.18035841 0.16575074 0.18561077] mean value: 0.17192482948303223 key: test_mcc value: [0.91464488 0.84628872 0.89959155 0.93218361 0.81310356 0.83358601 0.94884541 0.86518543 0.91392895 0.93158851] mean value: 0.8898946622870632 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.95652174 0.92307692 0.95 0.96610169 0.90434783 0.91071429 0.97478992 0.93442623 0.95726496 0.96610169] mean value: 0.9443345266578648 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.96491228 0.91525424 0.91935484 0.95 0.92857143 0.96226415 0.96666667 0.9047619 0.94915254 0.95 ] mean value: 0.9410938050015843 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.94827586 0.93103448 0.98275862 0.98275862 0.88135593 0.86440678 0.98305085 0.96610169 0.96551724 0.98275862] mean value: 0.948801870251315 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.95726496 0.92307692 0.94871795 0.96581197 0.90598291 0.91452991 0.97435897 0.93162393 0.95689655 0.96551724] mean value: 0.9443781314470968 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.95718878 0.92314436 0.94900643 0.96595558 0.90619521 0.91496201 0.97428404 0.93132671 0.95689655 0.96551724] mean value: 0.944447691408533 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.91666667 0.85714286 0.9047619 0.93442623 0.82539683 0.83606557 0.95081967 0.87692308 0.91803279 0.93442623] mean value: 0.8954661822694611 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.25 MCC on Training: 0.89 Running classifier: 19 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea...age_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42))]) key: fit_time value: [1.32056212 1.32144094 1.36794043 1.32781219 1.37780762 1.36977339 1.30637193 1.30138016 1.29643297 1.31405783] mean value: 1.3303579568862915 key: score_time value: [0.25039077 0.2063179 0.22491169 0.25715637 0.23157382 0.13629675 0.27747059 0.26421976 0.28325057 0.290272 ] mean value: 0.24218602180480958 key: test_mcc value: [0.91569228 0.78077709 0.86534091 0.8974284 0.74466247 0.74648621 0.91466978 0.82948262 0.86258195 0.86258195] mean value: 0.8419703671862353 key: train_mcc value: [0.96219269 0.96786253 0.9658593 0.9677361 0.95816253 0.96779125 0.96405445 0.96017821 0.96222762 0.96589119] mean value: 0.9641955859578871 key: test_fscore value: [0.95575221 0.89256198 0.93333333 0.94827586 0.86956522 0.86725664 0.95726496 0.91666667 0.92982456 0.93220339] mean value: 0.920270482098784 key: train_fscore value: [0.98076923 0.98366955 0.98275862 0.98373206 0.9789675 0.98366955 0.98171319 0.97982709 0.98076923 0.98275862] mean value: 0.9818634627668729 key: test_precision value: [0.98181818 0.85714286 0.90322581 0.94827586 0.89285714 0.90740741 0.96551724 0.90163934 0.94642857 0.91666667] mean value: 0.9220979081483011 key: train_precision value: [0.9922179 0.99417476 0.99034749 0.99036609 0.98272553 0.99224806 0.9922179 0.98837209 0.9922179 0.99034749] mean value: 0.9905235205976426 key: test_recall value: [0.93103448 0.93103448 0.96551724 0.94827586 0.84745763 0.83050847 0.94915254 0.93220339 0.9137931 0.94827586] mean value: 0.9197253068381064 key: train_recall value: [0.96958175 0.97338403 0.97528517 0.97718631 0.9752381 0.9752381 0.97142857 0.97142857 0.96958175 0.97528517] mean value: 0.973363751584284 key: test_accuracy value: [0.95726496 0.88888889 0.93162393 0.94871795 0.87179487 0.87179487 0.95726496 0.91452991 0.93103448 0.93103448] mean value: 0.9203949307397584 key: train_accuracy value: [0.9809705 0.98382493 0.98287345 0.98382493 0.97906755 0.98382493 0.98192198 0.98001903 0.98098859 0.98288973] mean value: 0.9820205634322555 key: test_roc_auc value: [0.95704267 0.88924605 0.93191116 0.9487142 0.87200468 0.87215079 0.95733489 0.91437756 0.93103448 0.93103448] mean value: 0.9204850964348334 key: train_roc_auc value: [0.98098135 0.98383487 0.98288068 0.98383125 0.97906391 0.98381677 0.981912 0.98001086 0.98098859 0.98288973] mean value: 0.9820210030780373 key: test_jcc value: [0.91525424 0.80597015 0.875 0.90163934 0.76923077 0.765625 0.91803279 0.84615385 0.86885246 0.87301587] mean value: 0.8538774465106289 key: train_jcc value: [0.96226415 0.96786389 0.96610169 0.96798493 0.9588015 0.96786389 0.96408318 0.96045198 0.96226415 0.96610169] mean value: 0.9643781065415575 MCC on Blind test: 0.29 MCC on Training: 0.84 Running classifier: 20 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.03933954 0.03482699 0.02257013 0.04433084 0.03502989 0.03511715 0.03503633 0.03511238 0.03537297 0.0393889 ] mean value: 0.03561251163482666 key: score_time value: [0.03021431 0.02565241 0.01504469 0.02950525 0.03062582 0.03047204 0.03015423 0.03004909 0.02868891 0.03031158] mean value: 0.028071832656860352 key: test_mcc value: [0.67533606 0.66472504 0.78740422 0.69277992 0.69228521 0.62415935 0.76104153 0.58652933 0.62068966 0.71210181] mean value: 0.6817052133603896 key: train_mcc value: [0.75130409 0.76495988 0.74761341 0.74971689 0.76315059 0.77275503 0.74579374 0.76449031 0.77295904 0.75905049] mean value: 0.7591793477695655 key: test_fscore value: [0.83760684 0.83870968 0.896 0.84745763 0.84745763 0.81666667 0.88333333 0.80916031 0.81034483 0.86178862] mean value: 0.8448525520079377 key: train_fscore value: [0.8779124 0.88497217 0.87627907 0.87755102 0.88393686 0.88868275 0.87523277 0.88389513 0.88888889 0.88141923] mean value: 0.8818770292503736 key: test_precision value: [0.83050847 0.78787879 0.8358209 0.83333333 0.84745763 0.80327869 0.86885246 0.73611111 0.81034483 0.81538462] mean value: 0.8168970820052343 key: train_precision value: [0.86106033 0.86413043 0.8579235 0.85688406 0.86231884 0.86618445 0.856102 0.86924494 0.86642599 0.86605505] mean value: 0.8626329585969273 key: test_recall value: [0.84482759 0.89655172 0.96551724 0.86206897 0.84745763 0.83050847 0.89830508 0.89830508 0.81034483 0.9137931 ] mean value: 0.8767679719462302 key: train_recall value: [0.89543726 0.90684411 0.89543726 0.89923954 0.90666667 0.91238095 0.8952381 0.89904762 0.91254753 0.8973384 ] mean value: 0.9020177439797212 key: test_accuracy value: [0.83760684 0.82905983 0.88888889 0.84615385 0.84615385 0.81196581 0.88034188 0.78632479 0.81034483 0.85344828] mean value: 0.8390288829944004 key: train_accuracy value: [0.8753568 0.88201713 0.87345385 0.87440533 0.88106565 0.88582303 0.87250238 0.88201713 0.88593156 0.87927757] mean value: 0.8791850419480994 key: test_roc_auc value: [0.83766803 0.82963179 0.88953828 0.84628872 0.84614261 0.81180596 0.88018703 0.78535944 0.81034483 0.85344828] mean value: 0.8390414962010521 key: train_roc_auc value: [0.87533768 0.88199348 0.87343292 0.87438168 0.88108999 0.88584827 0.87252399 0.88203332 0.88593156 0.87927757] mean value: 0.8791850443599494 key: test_jcc value: [0.72058824 0.72222222 0.8115942 0.73529412 0.73529412 0.69014085 0.79104478 0.67948718 0.68115942 0.75714286] mean value: 0.7323967973818726 key: train_jcc value: [0.78239203 0.7936772 0.77980132 0.78181818 0.79201331 0.79966611 0.7781457 0.79194631 0.8 0.78797997] mean value: 0.7887440129590283 MCC on Blind test: 0.23 MCC on Training: 0.68 Running classifier: 21 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=3))]) key: fit_time value: [0.13936377 0.13175344 0.17630839 0.11300826 0.1489892 0.14547801 0.13621593 0.13097978 0.13863587 0.14275312] mean value: 0.14034857749938964 key: score_time value: [0.01965785 0.01840186 0.01979589 0.01924133 0.01913977 0.0192337 0.01920176 0.01951885 0.02410674 0.0251925 ] mean value: 0.02034902572631836 key: test_mcc value: [0.62390415 0.65983708 0.8180103 0.74611818 0.65857939 0.65857939 0.79485681 0.58652933 0.60425713 0.71210181] mean value: 0.6862773575255877 key: train_mcc value: [0.79834139 0.80209816 0.78311695 0.78496754 0.7963983 0.81164286 0.78306355 0.76449031 0.80989179 0.75905049] mean value: 0.7893061332253646 key: test_fscore value: [0.81034483 0.83333333 0.91056911 0.86486486 0.82758621 0.82758621 0.89830508 0.80916031 0.79646018 0.86178862] mean value: 0.843999873023517 key: train_fscore value: [0.89866157 0.90132827 0.89101338 0.89268756 0.89838557 0.90526316 0.89142857 0.88389513 0.90512334 0.88141923] mean value: 0.8949205784278398 key: test_precision value: [0.81034483 0.80645161 0.86153846 0.90566038 0.84210526 0.84210526 0.89830508 0.73611111 0.81818182 0.81538462] mean value: 0.8336188435125482 key: train_precision value: [0.90384615 0.89962121 0.89615385 0.89184061 0.89583333 0.90961538 0.89142857 0.86924494 0.90340909 0.86605505] mean value: 0.8927048181033058 key: test_recall value: [0.81034483 0.86206897 0.96551724 0.82758621 0.81355932 0.81355932 0.89830508 0.89830508 0.77586207 0.9137931 ] mean value: 0.8578901227352427 key: train_recall value: [0.89353612 0.90304183 0.88593156 0.89353612 0.90095238 0.90095238 0.89142857 0.89904762 0.90684411 0.8973384 ] mean value: 0.8972609089263083 key: test_accuracy value: [0.81196581 0.82905983 0.90598291 0.87179487 0.82905983 0.82905983 0.8974359 0.78632479 0.80172414 0.85344828] mean value: 0.8415856174476865 key: train_accuracy value: [0.89914367 0.90104662 0.89153187 0.89248335 0.8981922 0.905804 0.89153187 0.88201713 0.90494297 0.87927757] mean value: 0.8945971245925481 key: test_roc_auc value: [0.81195207 0.82933957 0.90648743 0.87142022 0.82919345 0.82919345 0.8974284 0.78535944 0.80172414 0.85344828] mean value: 0.8415546464056108 key: train_roc_auc value: [0.89914901 0.90104472 0.89153721 0.89248235 0.89819482 0.90579938 0.89153178 0.88203332 0.90494297 0.87927757] mean value: 0.8945993119681332 key: test_jcc value: [0.68115942 0.71428571 0.8358209 0.76190476 0.70588235 0.70588235 0.81538462 0.67948718 0.66176471 0.75714286] mean value: 0.7318714855782078 key: train_jcc value: [0.81597222 0.82037997 0.80344828 0.80617496 0.81551724 0.82692308 0.80412371 0.79194631 0.82668977 0.78797997] mean value: 0.8099155500335481 MCC on Blind test: 0.21 MCC on Training: 0.69 Running classifier: 22 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.08959961 0.06347561 0.07022262 0.05824804 0.05776739 0.05833364 0.05779696 0.05860734 0.05826592 0.06977963] mean value: 0.0642096757888794 key: score_time value: [0.02444243 0.0221436 0.02263808 0.02544022 0.0221343 0.02198935 0.02196026 0.02196169 0.0214262 0.02397227] mean value: 0.022810840606689455 key: test_mcc value: [0.76066628 0.74466247 0.69622043 0.67524119 0.67622138 0.65857939 0.76066628 0.69228521 0.59511904 0.67251376] mean value: 0.6932175410023917 key: train_mcc value: [0.75144379 0.77185207 0.77787093 0.76840502 0.78722828 0.78908822 0.77572141 0.76229338 0.78140413 0.77097835] mean value: 0.7736285572648521 key: test_fscore value: [0.87931034 0.87394958 0.85245902 0.83478261 0.83478261 0.82758621 0.88135593 0.84745763 0.77358491 0.83478261] mean value: 0.8440051439018882 key: train_fscore value: [0.87269193 0.88461538 0.88673766 0.88178295 0.89168279 0.89275362 0.88610039 0.87969201 0.89016237 0.88195122] mean value: 0.884817031589981 key: test_precision value: [0.87931034 0.85245902 0.8125 0.84210526 0.85714286 0.84210526 0.88135593 0.84745763 0.85416667 0.84210526] mean value: 0.8510708233826272 key: train_precision value: [0.89264414 0.89494163 0.90335306 0.89920949 0.90569745 0.90588235 0.89823875 0.88910506 0.89443378 0.90581162] mean value: 0.8989317322065089 key: test_recall value: [0.87931034 0.89655172 0.89655172 0.82758621 0.81355932 0.81355932 0.88135593 0.84745763 0.70689655 0.82758621] mean value: 0.8390414962010521 key: train_recall value: [0.85361217 0.87452471 0.87072243 0.86501901 0.87809524 0.88 0.87428571 0.87047619 0.88593156 0.85931559] mean value: 0.8711982618142315 key: test_accuracy value: [0.88034188 0.87179487 0.84615385 0.83760684 0.83760684 0.82905983 0.88034188 0.84615385 0.79310345 0.8362069 ] mean value: 0.8458370173887415 key: train_accuracy value: [0.8753568 0.88582303 0.88867745 0.88392008 0.89343482 0.8943863 0.88772598 0.88106565 0.89068441 0.88498099] mean value: 0.8866055503901771 key: test_roc_auc value: [0.88033314 0.87200468 0.84658095 0.83752192 0.83781414 0.82919345 0.88033314 0.84614261 0.79310345 0.8362069 ] mean value: 0.8459234365867913 key: train_roc_auc value: [0.87537751 0.88583379 0.88869455 0.88393808 0.89342024 0.89437262 0.8877132 0.88105559 0.89068441 0.88498099] mean value: 0.8866070975918886 key: test_jcc value: [0.78461538 0.7761194 0.74285714 0.71641791 0.71641791 0.70588235 0.78787879 0.73529412 0.63076923 0.71641791] mean value: 0.731267015103714 key: train_jcc value: [0.77413793 0.79310345 0.79652174 0.78856153 0.80453752 0.80628272 0.79549393 0.78522337 0.8020654 0.78883072] mean value: 0.7934758309745898 MCC on Blind test: 0.24 MCC on Training: 0.69 Running classifier: 23 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.03382349 0.04499888 0.04151511 0.0388937 0.04943323 0.0519948 0.04960418 0.04977202 0.04175997 0.04293084] mean value: 0.04447262287139893 key: score_time value: [0.01030993 0.01233292 0.01254129 0.01247191 0.013098 0.01259232 0.01247263 0.01254678 0.01248431 0.01252055] mean value: 0.012337064743041993 key: test_mcc value: [0.64776143 0.67069032 0.65944011 0.59941805 0.45059674 0.34096357 0.64253819 0.47225957 0.580381 0.75862069] mean value: 0.5822669668133529 key: train_mcc value: [0.60228448 0.71269597 0.70848659 0.67878659 0.54026099 0.38767284 0.60540035 0.64072059 0.65543395 0.74464034] mean value: 0.6276382692161352 key: test_fscore value: [0.82857143 0.84210526 0.82142857 0.81203008 0.75862069 0.7195122 0.76767677 0.76595745 0.80597015 0.87931034] mean value: 0.8001182931689584 key: train_fscore value: [0.81289308 0.86271036 0.83677686 0.84791844 0.78797224 0.73031359 0.74603175 0.83090129 0.8369028 0.86777669] mean value: 0.8160197097375648 key: test_precision value: [0.70731707 0.74666667 0.85185185 0.72 0.63953488 0.56190476 0.95 0.65853659 0.71052632 0.87931034] mean value: 0.7425648483297855 key: train_precision value: [0.69302949 0.80762852 0.91628959 0.76651306 0.6619171 0.57582418 0.92156863 0.75625 0.73837209 0.89494949] mean value: 0.7732342153952378 key: test_recall value: [1. 0.96551724 0.79310345 0.93103448 0.93220339 1. 0.6440678 0.91525424 0.93103448 0.87931034] mean value: 0.8991525423728813 key: train_recall value: [0.98288973 0.92585551 0.76996198 0.9486692 0.97333333 0.99809524 0.62666667 0.92190476 0.96577947 0.84220532] mean value: 0.8955361216730038 key: test_accuracy value: [0.79487179 0.82051282 0.82905983 0.78632479 0.7008547 0.60683761 0.8034188 0.71794872 0.77586207 0.87931034] mean value: 0.7715001473622163 key: train_accuracy value: [0.773549 0.85252141 0.84966698 0.82968601 0.73834443 0.63177926 0.78686965 0.81255947 0.81178707 0.871673 ] mean value: 0.795843628917598 key: test_roc_auc value: [0.79661017 0.82174167 0.82875511 0.78755114 0.69886032 0.60344828 0.80479252 0.71624781 0.77586207 0.87931034] mean value: 0.7713179427235535 key: train_roc_auc value: [0.77334963 0.85245157 0.84974289 0.8295727 0.73856781 0.63212747 0.78671736 0.81266341 0.81178707 0.871673 ] mean value: 0.7958652906029332 key: test_jcc value: [0.70731707 0.72727273 0.6969697 0.6835443 0.61111111 0.56190476 0.62295082 0.62068966 0.675 0.78461538] mean value: 0.6691375533686428 key: train_jcc value: [0.68476821 0.75856698 0.71936057 0.7359882 0.65012723 0.5751921 0.59493671 0.71071953 0.71954674 0.76643599] mean value: 0.6915642249479907 MCC on Blind test: 0.08 MCC on Training: 0.58 Running classifier: 24 Model_name: XGBoost Model func: /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:463: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_CV['source_data'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:490: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_BT['source_data'] = 'BT' XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.25532532 0.20894623 0.21842885 0.35953784 0.27700257 0.20583248 0.2021594 0.21225476 0.23515558 0.21612263] mean value: 0.239076566696167 key: score_time value: [0.0117507 0.01184487 0.01220441 0.01249313 0.01169419 0.01145458 0.01130462 0.01216221 0.01205015 0.01204562] mean value: 0.0119004487991333 key: test_mcc value: [0.93161894 0.84935886 0.91794064 0.91466978 0.84628872 0.76104153 0.8974284 0.86370317 0.93103448 0.94954692] mean value: 0.8862631446494762 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.96551724 0.92561983 0.95867769 0.95726496 0.92307692 0.88333333 0.94915254 0.93333333 0.96551724 0.97478992] mean value: 0.9436283008767592 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.96551724 0.88888889 0.92063492 0.94915254 0.93103448 0.86885246 0.94915254 0.91803279 0.96551724 0.95081967] mean value: 0.93076027778196 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.96551724 0.96551724 1. 0.96551724 0.91525424 0.89830508 0.94915254 0.94915254 0.96551724 1. ] mean value: 0.9573933372296903 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.96581197 0.92307692 0.95726496 0.95726496 0.92307692 0.88034188 0.94871795 0.93162393 0.96551724 0.97413793] mean value: 0.942683465959328 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.96580947 0.92343659 0.95762712 0.95733489 0.92314436 0.88018703 0.9487142 0.93147282 0.96551724 0.97413793] mean value: 0.9427381648158972 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.93333333 0.86153846 0.92063492 0.91803279 0.85714286 0.79104478 0.90322581 0.875 0.93333333 0.95081967] mean value: 0.8944105947570316 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.28 MCC on Training: 0.89 Extracting tts_split_name: 80_20 Total cols in each df: CV df: 8 metaDF: 17 Adding column: Model_name Total cols in bts df: BT_df: 8 First proceeding to rowbind CV and BT dfs: Final output should have: 25 columns Combinig 2 using pd.concat by row ~ rowbind Checking Dims of df to combine: Dim of CV: (24, 8) Dim of BT: (24, 8) 8 Number of Common columns: 8 These are: ['source_data', 'Precision', 'JCC', 'MCC', 'Recall', 'Accuracy', 'F1', 'ROC_AUC'] Concatenating dfs with different resampling methods [WF]: Split type: 80_20 No. of dfs combining: 2 PASS: 2 dfs successfully combined nrows in combined_df_wf: 48 ncols in combined_df_wf: 8 PASS: proceeding to merge metadata with CV and BT dfs Adding column: Model_name ========================================================= SUCCESS: Ran multiple classifiers ======================================================= ============================================================== Running several classification models (n): 24 List of models: ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('Gaussian NB', GaussianNB()) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('K-Nearest Neighbors', KNeighborsClassifier()) ('LDA', LinearDiscriminantAnalysis()) ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(cv=3, random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Multinomial', MultinomialNB()) ('Naive Bayes', BernoulliNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=12, random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Random Forest', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42)) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=3)) ('SVC', SVC(random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=12, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ================================================================ Running classifier: 1 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. 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Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.6s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.7s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.6s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.4s remaining: 0.7s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.6s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.7s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.7s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.4s remaining: 0.7s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.6s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.7s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 2.8s remaining: 5.5s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 2.9s remaining: 5.7s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 2.9s remaining: 5.7s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 2.9s remaining: 5.8s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 3.0s remaining: 1.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 3.0s remaining: 6.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 3.0s remaining: 6.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 3.0s remaining: 1.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 3.0s remaining: 1.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 3.0s remaining: 1.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 3.0s remaining: 6.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 3.1s remaining: 6.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 3.1s remaining: 1.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 3.1s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 3.1s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 3.2s remaining: 6.3s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 3.2s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 3.1s remaining: 1.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 3.2s remaining: 1.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 3.2s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 3.2s remaining: 1.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 3.2s remaining: 1.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 3.2s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 3.2s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 3.2s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 3.2s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 3.2s remaining: 6.4s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 3.2s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 3.3s remaining: 1.1s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 3.3s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.7s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.27816391 0.28221154 0.29781008 0.29151011 0.29981208 0.30334759 0.30310249 0.29630852 0.2939899 0.29053783] mean value: 0.293679404258728 key: score_time value: [0.0161159 0.01681924 0.01815248 0.01797009 0.01741266 0.01739526 0.01741362 0.01678467 0.01671958 0.01801443] mean value: 0.017279791831970214 key: test_mcc value: [0.7492057 0.76814635 0.77235167 0.76066628 0.77868973 0.60743653 0.71018845 0.65944011 0.66149509 0.67251376] mean value: 0.7140133665162441 key: train_mcc value: [0.792987 0.85021988 0.85811025 0.84032411 0.79715652 0.85115742 0.83191384 0.84573943 0.85809859 0.81900751] mean value: 0.8344714531453565 key: test_fscore value: [0.87804878 0.88709677 0.88888889 0.87931034 0.89256198 0.80991736 0.85950413 0.83606557 0.83870968 0.83760684] mean value: 0.8607710348268893 key: train_fscore value: [0.89803555 0.92673993 0.93053016 0.92194674 0.90027959 0.92691952 0.91751622 0.92435424 0.93010252 0.91143911] mean value: 0.9187863580505432 key: test_precision value: [0.83076923 0.83333333 0.82352941 0.87931034 0.87096774 0.79032258 0.83870968 0.80952381 0.78787879 0.83050847] mean value: 0.8294853392673724 key: train_precision value: [0.8839779 0.89399293 0.89612676 0.89165187 0.88138686 0.90107914 0.89350181 0.89624329 0.91224863 0.88530466] mean value: 0.893551384202077 key: test_recall value: [0.93103448 0.94827586 0.96551724 0.87931034 0.91525424 0.83050847 0.88135593 0.86440678 0.89655172 0.84482759] mean value: 0.8957042665108125 key: train_recall value: [0.91254753 0.96197719 0.96768061 0.95437262 0.92 0.95428571 0.94285714 0.95428571 0.9486692 0.9391635 ] mean value: 0.9455839217816404 key: test_accuracy value: [0.87179487 0.88034188 0.88034188 0.88034188 0.88888889 0.8034188 0.85470085 0.82905983 0.82758621 0.8362069 ] mean value: 0.8552681992337166 key: train_accuracy value: [0.89628925 0.92388202 0.92768792 0.91912464 0.8981922 0.92483349 0.91531874 0.92197907 0.92870722 0.90874525] mean value: 0.9164759797838741 key: test_roc_auc value: [0.8722969 0.88091759 0.88106371 0.88033314 0.8886616 0.80318527 0.85447107 0.82875511 0.82758621 0.8362069 ] mean value: 0.8553477498538866 key: train_roc_auc value: [0.89627376 0.92384574 0.92764983 0.91909107 0.89821293 0.92486149 0.91534492 0.92200978 0.92870722 0.90874525] mean value: 0.9164741988049971 key: test_jcc value: [0.7826087 0.79710145 0.8 0.78461538 0.80597015 0.68055556 0.75362319 0.71830986 0.72222222 0.72058824] mean value: 0.7565594739429276 key: train_jcc value: [0.81494058 0.86348123 0.87008547 0.85519591 0.81864407 0.8637931 0.84760274 0.8593482 0.86933798 0.83728814] mean value: 0.8499717412047009 MCC on Blind test: 0.26 MCC on Training: 0.71 Running classifier: 2 Model_name: Bagging Classifier Model func: BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3))]) key: fit_time value: [0.52303338 0.56587839 0.59346151 0.61186075 0.56738496 0.50092411 0.61202478 0.62197423 0.61153555 0.61350727] mean value: 0.5821584939956665 key: score_time value: [0.05494547 0.07248664 0.07018042 0.07813072 0.0642271 0.07777357 0.07928395 0.04324341 0.07447791 0.05722737] mean value: 0.06719765663146973 key: test_mcc value: [0.96638414 0.88681491 0.94998574 0.93384219 0.90210482 0.9337672 0.98304594 0.90210482 0.93325653 0.91720763] mean value: 0.9308513906126367 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.98305085 0.94308943 0.97478992 0.96666667 0.9516129 0.96721311 0.99159664 0.9516129 0.96666667 0.95867769] mean value: 0.9654976773463242 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.96666667 0.89230769 0.95081967 0.93548387 0.90769231 0.93650794 0.98333333 0.90769231 0.93548387 0.92063492] mean value: 0.9336622578901796 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.98290598 0.94017094 0.97435897 0.96581197 0.94871795 0.96581197 0.99145299 0.94871795 0.96551724 0.95689655] mean value: 0.9640362511052164 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.98305085 0.94067797 0.97457627 0.96610169 0.94827586 0.96551724 0.99137931 0.94827586 0.96551724 0.95689655] mean value: 0.9640268848626533 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.96666667 0.89230769 0.95081967 0.93548387 0.90769231 0.93650794 0.98333333 0.90769231 0.93548387 0.92063492] mean value: 0.9336622578901796 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.28 MCC on Training: 0.93 Running classifier: 3 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.05528712 0.05279636 0.0419054 0.04346633 0.04784918 0.04357457 0.04582334 0.04506898 0.04399133 0.04715157] mean value: 0.0466914176940918 key: score_time value: [0.00929451 0.00923157 0.00940371 0.01040792 0.00969648 0.00976753 0.01032066 0.01010585 0.01019979 0.01003003] mean value: 0.009845805168151856 key: test_mcc value: [0.9022688 0.84165009 0.9022688 0.87156767 0.84121708 0.8865947 0.9337672 0.82644112 0.91720763 0.91720763] mean value: 0.8840190717019427 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.95081967 0.92063492 0.95081967 0.93548387 0.921875 0.944 0.96721311 0.91472868 0.95867769 0.95867769] mean value: 0.9422930304690423 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.90625 0.85294118 0.90625 0.87878788 0.85507246 0.89393939 0.93650794 0.84285714 0.92063492 0.92063492] mean value: 0.8913875833600897 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.94871795 0.91452991 0.94871795 0.93162393 0.91452991 0.94017094 0.96581197 0.90598291 0.95689655 0.95689655] mean value: 0.9383878573533746 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.94915254 0.91525424 0.94915254 0.93220339 0.9137931 0.93965517 0.96551724 0.90517241 0.95689655 0.95689655] mean value: 0.9383693746347166 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.90625 0.85294118 0.90625 0.87878788 0.85507246 0.89393939 0.93650794 0.84285714 0.92063492 0.92063492] mean value: 0.8913875833600897 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.14 MCC on Training: 0.88 Running classifier: 4 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.01279736 0.01318526 0.0138886 0.01301622 0.01257467 0.01379848 0.013345 0.01335812 0.01360846 0.01434541] mean value: 0.013391757011413574 key: score_time value: [0.00905919 0.01002765 0.01011992 0.00957799 0.00948048 0.00951385 0.00997233 0.01024342 0.01005435 0.00989294] mean value: 0.009794211387634278 key: test_mcc value: [0.88681491 0.85651622 0.84165009 0.87156767 0.84121708 0.90210482 0.84121708 0.91782516 0.84016805 0.90138782] mean value: 0.8700468890800984 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.94308943 0.928 0.92063492 0.93548387 0.921875 0.9516129 0.921875 0.95934959 0.92063492 0.95081967] mean value: 0.9353375311984783 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.89230769 0.86567164 0.85294118 0.87878788 0.85507246 0.90769231 0.85507246 0.921875 0.85294118 0.90625 ] mean value: 0.878861180105633 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.94017094 0.92307692 0.91452991 0.93162393 0.91452991 0.94871795 0.91452991 0.95726496 0.9137931 0.94827586] mean value: 0.9306513409961686 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.94067797 0.92372881 0.91525424 0.93220339 0.9137931 0.94827586 0.9137931 0.95689655 0.9137931 0.94827586] mean value: 0.9306691992986558 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.89230769 0.86567164 0.85294118 0.87878788 0.85507246 0.90769231 0.85507246 0.921875 0.85294118 0.90625 ] mean value: 0.878861180105633 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.08 MCC on Training: 0.87 Running classifier: 5 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.19348979 0.19126225 0.17370582 0.17572618 0.17362261 0.17636824 0.18746471 0.20730376 0.19985366 0.19359159] mean value: 0.18723886013031005 key: score_time value: [0.02046299 0.01881862 0.01882863 0.01893854 0.02004576 0.018893 0.02034593 0.02306461 0.02054906 0.01926088] mean value: 0.01992080211639404 key: test_mcc value: [0.98305085 0.96638414 0.96638414 0.96638414 0.98304594 0.98304594 0.98304594 0.9337672 0.98290472 0.98290472] mean value: 0.9730917716614929 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.99145299 0.98305085 0.98305085 0.98305085 0.99159664 0.99159664 0.99159664 0.96721311 0.99145299 0.99145299] mean value: 0.9865514547452341 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.98305085 0.96666667 0.96666667 0.96666667 0.98333333 0.98333333 0.98333333 0.93650794 0.98305085 0.98305085] mean value: 0.9735660478880817 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.99145299 0.98290598 0.98290598 0.98290598 0.99145299 0.99145299 0.99145299 0.96581197 0.99137931 0.99137931] mean value: 0.9863100501031535 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.99152542 0.98305085 0.98305085 0.98305085 0.99137931 0.99137931 0.99137931 0.96551724 0.99137931 0.99137931] mean value: 0.9863091759205143 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.98305085 0.96666667 0.96666667 0.96666667 0.98333333 0.98333333 0.98333333 0.93650794 0.98305085 0.98305085] mean value: 0.9735660478880817 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.07 MCC on Training: 0.97 Running classifier: 6 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [1.20145726 1.2119658 1.21881914 1.20981526 1.22245145 1.2177825 1.22020102 1.25144315 1.27898622 1.249856 ] mean value: 1.2282777786254884 key: score_time value: [0.0094974 0.00939131 0.00949955 0.00935674 0.00977802 0.00943923 0.01064849 0.01029325 0.01042414 0.01035619] mean value: 0.009868431091308593 key: test_mcc value: [0.91794064 0.9022688 0.85651622 0.93384219 0.86770252 0.8865947 0.96636481 0.84914237 0.91720763 0.88578518] mean value: 0.8983365040949438 key: train_mcc value: [0.9792817 0.98114781 0.98301734 0.97741897 0.97727816 0.98114849 0.97370495 0.98484075 0.97744071 0.98303364] mean value: 0.9798312525947466 key: test_fscore value: [0.95867769 0.95081967 0.928 0.96666667 0.93548387 0.944 0.98333333 0.92682927 0.95867769 0.94308943] mean value: 0.9495577614186708 key: train_fscore value: [0.98965193 0.9905838 0.99151744 0.9887218 0.98865784 0.99056604 0.98684211 0.99242424 0.9887218 0.99151744] mean value: 0.989920444484526 key: test_precision value: [0.92063492 0.90625 0.86567164 0.93548387 0.89230769 0.89393939 0.96721311 0.890625 0.92063492 0.89230769] mean value: 0.9085068247337504 key: train_precision value: [0.97951583 0.98134328 0.98317757 0.97769517 0.98123827 0.98130841 0.97402597 0.98681733 0.97769517 0.98317757] mean value: 0.9805994571981842 key: test_recall value: [1. 1. 1. 1. 0.98305085 1. 1. 0.96610169 1. 1. ] mean value: 0.9949152542372882 key: train_recall value: [1. 1. 1. 1. 0.99619048 1. 1. 0.99809524 1. 1. ] mean value: 0.9994285714285714 key: test_accuracy value: [0.95726496 0.94871795 0.92307692 0.96581197 0.93162393 0.94017094 0.98290598 0.92307692 0.95689655 0.93965517] mean value: 0.9469201296787503 key: train_accuracy value: [0.98953378 0.99048525 0.99143673 0.9885823 0.9885823 0.99048525 0.98667935 0.9923882 0.98859316 0.99144487] mean value: 0.9898211191224725 key: test_roc_auc value: [0.95762712 0.94915254 0.92372881 0.96610169 0.9311806 0.93965517 0.98275862 0.92270602 0.95689655 0.93965517] mean value: 0.9469462302746934 key: train_roc_auc value: [0.98952381 0.99047619 0.99142857 0.98857143 0.98858953 0.9904943 0.98669202 0.99239363 0.98859316 0.99144487] mean value: 0.9898207495926126 key: test_jcc value: [0.92063492 0.90625 0.86567164 0.93548387 0.87878788 0.89393939 0.96721311 0.86363636 0.92063492 0.89230769] mean value: 0.9044559797454055 key: train_jcc value: [0.97951583 0.98134328 0.98317757 0.97769517 0.97757009 0.98130841 0.97402597 0.98496241 0.97769517 0.98317757] mean value: 0.9800471471733246 MCC on Blind test: 0.39 MCC on Training: 0.9 Running classifier: 7 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.01320958 0.01329303 0.01345921 0.01294494 0.01200747 0.01170373 0.01195478 0.01178646 0.01179171 0.01188207] mean value: 0.012403297424316406 key: score_time value: [0.01001334 0.010288 0.01019597 0.00906968 0.00909781 0.00906348 0.00908971 0.00911117 0.00910807 0.009166 ] mean value: 0.009420323371887206 key: test_mcc value: [0.50572841 0.40181181 0.47398728 0.21457879 0.31657411 0.43667756 0.30362023 0.40357735 0.48275862 0.44854261] mean value: 0.3987856770088429 key: train_mcc value: [0.40959267 0.45859631 0.44149169 0.42534268 0.41978666 0.44072045 0.41995736 0.43305986 0.4326306 0.38598806] mean value: 0.42671663285220374 key: test_fscore value: [0.73873874 0.7008547 0.74796748 0.61666667 0.65517241 0.71304348 0.62385321 0.69026549 0.74137931 0.71929825] mean value: 0.6947239731682576 key: train_fscore value: [0.68356998 0.72031403 0.71119843 0.71128107 0.71361502 0.71566731 0.71521942 0.71235521 0.70541872 0.69557022] mean value: 0.7084209416450139 key: test_precision value: [0.77358491 0.69491525 0.70769231 0.59677419 0.66666667 0.73214286 0.68 0.72222222 0.74137931 0.73214286] mean value: 0.7047520574657791 key: train_precision value: [0.7326087 0.74442191 0.73577236 0.71538462 0.7037037 0.72691552 0.7014652 0.7221135 0.73210634 0.68971963] mean value: 0.7204211469823615 key: test_recall value: [0.70689655 0.70689655 0.79310345 0.63793103 0.6440678 0.69491525 0.57627119 0.66101695 0.74137931 0.70689655] mean value: 0.6869374634716541 key: train_recall value: [0.64068441 0.69771863 0.68821293 0.70722433 0.72380952 0.7047619 0.72952381 0.70285714 0.68060837 0.70152091] mean value: 0.697692196270143 key: test_accuracy value: [0.75213675 0.7008547 0.73504274 0.60683761 0.65811966 0.71794872 0.64957265 0.7008547 0.74137931 0.72413793] mean value: 0.6986884762746832 key: train_accuracy value: [0.70313987 0.72882969 0.72026641 0.71265461 0.70980019 0.72026641 0.70980019 0.71646051 0.71577947 0.69296578] mean value: 0.7129963134874264 key: test_roc_auc value: [0.75175336 0.7009059 0.73553477 0.60710111 0.65824079 0.71814728 0.65020456 0.70119813 0.74137931 0.72413793] mean value: 0.6988603156049094 key: train_roc_auc value: [0.70319935 0.72885932 0.72029694 0.71265979 0.70981351 0.72025167 0.70981894 0.71644758 0.71577947 0.69296578] mean value: 0.7130092341118959 key: test_jcc value: [0.58571429 0.53947368 0.5974026 0.44578313 0.48717949 0.55405405 0.45333333 0.52702703 0.5890411 0.56164384] mean value: 0.5340652532958281 key: train_jcc value: [0.5192604 0.56288344 0.55182927 0.55192878 0.55474453 0.55722892 0.55668605 0.55322339 0.54490107 0.53323699] mean value: 0.5485922823570861 MCC on Blind test: 0.26 MCC on Training: 0.4 Running classifier: 8 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.63573694 0.58996916 0.78658938 0.50850272 0.49798036 0.60596395 0.49960041 0.66270137 0.6308341 0.50858235] mean value: 0.5926460742950439 key: score_time value: [0.05097628 0.04902911 0.04685593 0.04996681 0.04272103 0.05238724 0.04865408 0.03849077 0.04336524 0.0242753 ] mean value: 0.044672179222106936 key: test_mcc value: [0.88681491 0.71480339 0.78740422 0.76814635 0.8865947 0.78045957 0.81764715 0.70220239 0.85518611 0.78157516] mean value: 0.7980833962593759 key: train_mcc value: [0.93611786 0.93611786 0.93216668 0.92987374 0.95532791 0.92627197 0.94557177 0.93414537 0.93849958 0.93815966] mean value: 0.9372252398768743 key: test_fscore value: [0.94308943 0.86178862 0.896 0.88709677 0.944 0.89430894 0.912 0.859375 0.928 0.89430894] mean value: 0.90199677091529 key: train_fscore value: [0.96834264 0.96834264 0.96641791 0.96525822 0.97765363 0.96344892 0.9729225 0.96732026 0.96941613 0.96930233] mean value: 0.9688425183943726 key: test_precision value: [0.89230769 0.81538462 0.8358209 0.83333333 0.89393939 0.859375 0.86363636 0.79710145 0.86567164 0.84615385] mean value: 0.850272423134404 key: train_precision value: [0.94890511 0.94890511 0.94871795 0.95361781 0.95628415 0.94833948 0.95421245 0.94871795 0.94575045 0.94899818] mean value: 0.9502448648373362 key: test_recall value: [1. 0.9137931 0.96551724 0.94827586 1. 0.93220339 0.96610169 0.93220339 1. 0.94827586] mean value: 0.9606370543541788 key: train_recall value: [0.98859316 0.98859316 0.98479087 0.97718631 1. 0.97904762 0.99238095 0.98666667 0.99429658 0.9904943 ] mean value: 0.988204961071881 key: test_accuracy value: [0.94017094 0.85470085 0.88888889 0.88034188 0.94017094 0.88888889 0.90598291 0.84615385 0.92241379 0.88793103] mean value: 0.8955643972885351 key: train_accuracy value: [0.96764986 0.96764986 0.96574691 0.96479543 0.97716461 0.96289248 0.97240723 0.96669838 0.96863118 0.96863118] mean value: 0.9682267114788378 key: test_roc_auc value: [0.94067797 0.85520164 0.88953828 0.88091759 0.93965517 0.88851549 0.90546464 0.84541204 0.92241379 0.88793103] mean value: 0.8955727644652249 key: train_roc_auc value: [0.96762991 0.96762991 0.96572877 0.96478363 0.97718631 0.96290784 0.97242622 0.96671736 0.96863118 0.96863118] mean value: 0.9682272315770414 key: test_jcc value: [0.89230769 0.75714286 0.8115942 0.79710145 0.89393939 0.80882353 0.83823529 0.75342466 0.86567164 0.80882353] mean value: 0.8227064247830326 key: train_jcc value: [0.93862816 0.93862816 0.93501805 0.93284936 0.95628415 0.92947559 0.94727273 0.93670886 0.94064748 0.94043321] mean value: 0.9395945756774235 MCC on Blind test: 0.08 MCC on Training: 0.8 Running classifier: 9 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.01583171 0.0125339 0.01217675 0.01243854 0.01251793 0.01258588 0.01237845 0.0119555 0.0113318 0.01122832] mean value: 0.012497878074645996 key: score_time value: [0.04893684 0.01613426 0.01746726 0.02030468 0.01620579 0.01698375 0.01752472 0.02056694 0.01628637 0.01854706] mean value: 0.020895767211914062 key: test_mcc value: [0.65319081 0.67842794 0.60649937 0.68488609 0.7477368 0.65485915 0.56812364 0.57562495 0.76073435 0.64972301] mean value: 0.6579806105098032 key: train_mcc value: [0.76448106 0.77941237 0.77711948 0.77530739 0.77380367 0.79211772 0.78236579 0.78271473 0.79205077 0.77414688] mean value: 0.7793519858146233 key: test_fscore value: [0.83464567 0.84615385 0.81481481 0.84848485 0.87878788 0.83823529 0.80291971 0.8057554 0.88372093 0.83464567] mean value: 0.8388164054886922 key: train_fscore value: [0.88547009 0.89212329 0.89117395 0.89002558 0.8890785 0.89767842 0.89369058 0.89347079 0.8974359 0.88964927] mean value: 0.8919796354847648 key: test_precision value: [0.76811594 0.76388889 0.71428571 0.75675676 0.79452055 0.74025974 0.70512821 0.7 0.8028169 0.76811594] mean value: 0.7513888638730932 key: train_precision value: [0.80434783 0.81152648 0.81123245 0.80680062 0.80525502 0.81818182 0.81803797 0.81377152 0.81521739 0.80870918] mean value: 0.8113080274462959 key: test_recall value: [0.9137931 0.94827586 0.94827586 0.96551724 0.98305085 0.96610169 0.93220339 0.94915254 0.98275862 0.9137931 ] mean value: 0.950292226767972 key: train_recall value: [0.98479087 0.9904943 0.98859316 0.99239544 0.99238095 0.99428571 0.9847619 0.99047619 0.99809886 0.98859316] mean value: 0.9904870541372442 key: test_accuracy value: [0.82051282 0.82905983 0.78632479 0.82905983 0.86324786 0.81196581 0.76923077 0.76923077 0.87068966 0.81896552] mean value: 0.8168287651046272 key: train_accuracy value: [0.87250238 0.88011418 0.8791627 0.87725975 0.87630828 0.8867745 0.8829686 0.88201713 0.88593156 0.87737643] mean value: 0.8800415501441682 key: test_roc_auc value: [0.82130333 0.83007013 0.78769725 0.83021625 0.86221508 0.81063705 0.76782583 0.76767972 0.87068966 0.81896552] mean value: 0.8167299824663938 key: train_roc_auc value: [0.87239544 0.88000905 0.87905848 0.8771501 0.87641861 0.8868767 0.88306536 0.88212022 0.88593156 0.87737643] mean value: 0.8800401955458991 key: test_jcc value: [0.71621622 0.73333333 0.6875 0.73684211 0.78378378 0.72151899 0.67073171 0.6746988 0.79166667 0.71621622] mean value: 0.7232507811318942 key: train_jcc value: [0.79447853 0.80525502 0.80370943 0.80184332 0.80030722 0.81435257 0.8078125 0.80745342 0.81395349 0.80123267] mean value: 0.8050398160819097 MCC on Blind test: -0.01 MCC on Training: 0.66 Running classifier: 10 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.05858278 0.07154489 0.05901217 0.08546972 0.06258249 0.09601116 0.06718969 0.06369472 0.11611414 0.09025383] mean value: 0.07704555988311768 key: score_time value: [0.01922464 0.01245713 0.0123055 0.01291108 0.0171032 0.02650833 0.01244903 0.01931143 0.03237438 0.01941466] mean value: 0.018405938148498537 key: test_mcc value: [0.61462423 0.58006823 0.66840403 0.72833836 0.62415935 0.62415935 0.61361681 0.4968457 0.62068966 0.65870648] mean value: 0.6229612202696967 key: train_mcc value: [0.72987183 0.75097126 0.75322025 0.74100549 0.72789921 0.75460861 0.73955702 0.75621377 0.71416912 0.70992778] mean value: 0.7377444337729473 key: test_fscore value: [0.816 0.8 0.84126984 0.86666667 0.81666667 0.81666667 0.81889764 0.76923077 0.81034483 0.83606557] mean value: 0.8191808649652584 key: train_fscore value: [0.86892759 0.87912088 0.8803653 0.875 0.86764706 0.88051471 0.8733945 0.88110599 0.86057248 0.85793872] mean value: 0.8724587218698355 key: test_precision value: [0.76119403 0.74626866 0.77941176 0.83870968 0.80327869 0.80327869 0.76470588 0.70422535 0.81034483 0.796875 ] mean value: 0.7808292567793405 key: train_precision value: [0.83893805 0.84805654 0.84710018 0.83564014 0.8383659 0.85079929 0.84247788 0.85357143 0.83662478 0.8384755 ] mean value: 0.8430049670209602 key: test_recall value: [0.87931034 0.86206897 0.9137931 0.89655172 0.83050847 0.83050847 0.88135593 0.84745763 0.81034483 0.87931034] mean value: 0.8631209818819402 key: train_recall value: [0.90114068 0.91254753 0.91634981 0.91825095 0.89904762 0.91238095 0.90666667 0.91047619 0.88593156 0.878327 ] mean value: 0.9041118957088539 key: test_accuracy value: [0.8034188 0.78632479 0.82905983 0.86324786 0.81196581 0.81196581 0.8034188 0.74358974 0.81034483 0.82758621] mean value: 0.8090922487474211 key: train_accuracy value: [0.86393911 0.87440533 0.8753568 0.86869648 0.86298763 0.87630828 0.86869648 0.87725975 0.85646388 0.85456274] mean value: 0.8678676473248362 key: test_roc_auc value: [0.80406195 0.78696669 0.82977791 0.8635301 0.81180596 0.81180596 0.80274693 0.74269433 0.81034483 0.82758621] mean value: 0.8091320864991234 key: train_roc_auc value: [0.86390368 0.874369 0.87531776 0.86864928 0.86302191 0.87634257 0.86873257 0.87729133 0.85646388 0.85456274] mean value: 0.8678654716639507 key: test_jcc value: [0.68918919 0.66666667 0.7260274 0.76470588 0.69014085 0.69014085 0.69333333 0.625 0.68115942 0.71830986] mean value: 0.6944673438388035 key: train_jcc value: [0.76823339 0.78431373 0.7862969 0.77777778 0.76623377 0.7865353 0.7752443 0.78747941 0.75526742 0.75121951] mean value: 0.7738601502929255 MCC on Blind test: 0.12 MCC on Training: 0.62 Running classifier: 11 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.05584478 0.04687929 0.04990578 0.07659888 0.08085036 0.0489614 0.04779434 0.04869676 0.04877067 0.05893612] mean value: 0.05632383823394775 key: score_time value: [0.0128696 0.01276183 0.01258993 0.01876974 0.01294446 0.01305509 0.01294136 0.01253033 0.0134604 0.01304984] mean value: 0.013497257232666015 key: test_mcc value: [0.67524119 0.49235618 0.58006823 0.57276447 0.55597781 0.50468462 0.57719418 0.35037989 0.5690501 0.51754917] mean value: 0.5395265834843325 key: train_mcc value: [0.66718979 0.63672262 0.63694029 0.65485822 0.62944137 0.68647265 0.598508 0.65203504 0.66207572 0.62941521] mean value: 0.6453658914530399 key: test_fscore value: [0.83478261 0.75806452 0.8 0.78632479 0.77586207 0.76033058 0.77477477 0.6779661 0.78632479 0.76271186] mean value: 0.7717142085828641 key: train_fscore value: [0.83912249 0.82065728 0.821662 0.83210332 0.81792717 0.84565014 0.8 0.82816901 0.83395522 0.81655691] mean value: 0.8255803545994682 key: test_precision value: [0.84210526 0.71212121 0.74626866 0.77966102 0.78947368 0.74193548 0.82692308 0.6779661 0.77966102 0.75 ] mean value: 0.7646115512593317 key: train_precision value: [0.80809859 0.81076067 0.80733945 0.80824373 0.8021978 0.83088235 0.79622642 0.81666667 0.81868132 0.80819367] mean value: 0.8107290660702839 key: test_recall value: [0.82758621 0.81034483 0.86206897 0.79310345 0.76271186 0.77966102 0.72881356 0.6779661 0.79310345 0.77586207] mean value: 0.7811221507890123 key: train_recall value: [0.87262357 0.83079848 0.8365019 0.85741445 0.83428571 0.86095238 0.80380952 0.84 0.84980989 0.82509506] mean value: 0.8411290965055225 key: test_accuracy value: [0.83760684 0.74358974 0.78632479 0.78632479 0.77777778 0.75213675 0.78632479 0.67521368 0.78448276 0.75862069] mean value: 0.7688402593575008 key: train_accuracy value: [0.83254044 0.81826832 0.81826832 0.82683159 0.81446242 0.84300666 0.79923882 0.82588011 0.83079848 0.81463878] mean value: 0.8223933932195664 key: test_roc_auc value: [0.83752192 0.74415546 0.78696669 0.78638223 0.77790766 0.75189947 0.78682057 0.67518995 0.78448276 0.75862069] mean value: 0.7689947399181765 key: train_roc_auc value: [0.83250226 0.81825638 0.81825095 0.82680246 0.81448126 0.84302372 0.79924316 0.82589354 0.83079848 0.81463878] mean value: 0.8223891001267427 key: test_jcc value: [0.71641791 0.61038961 0.66666667 0.64788732 0.63380282 0.61333333 0.63235294 0.51282051 0.64788732 0.61643836] mean value: 0.629799679578747 key: train_jcc value: [0.72283465 0.69585987 0.69730586 0.71248025 0.69194313 0.73257699 0.66666667 0.70673077 0.7152 0.6899841 ] mean value: 0.7031582285775387 MCC on Blind test: 0.21 MCC on Training: 0.54 Running classifier: 12 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(cv=3, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(cv=3, random_state=42))]) key: fit_time value: [0.60545588 0.61257577 0.75680637 0.63767958 0.62618327 0.778193 0.70226312 0.63554382 0.72543931 0.61564922] mean value: 0.6695789337158203 key: score_time value: [0.01238394 0.01289988 0.01891494 0.01241422 0.01258183 0.01236415 0.01236582 0.01250505 0.01237011 0.01254463] mean value: 0.013134455680847168 key: test_mcc value: [0.62753763 0.57503178 0.68373622 0.71044192 0.695691 0.55656745 0.62939175 0.46184814 0.62217102 0.73514704] mean value: 0.6297563970928614 key: train_mcc value: [0.74738748 0.76180976 0.76180976 0.75781463 0.75989115 0.75004718 0.74409685 0.76498195 0.72491256 0.71154115] mean value: 0.7484292466032019 key: test_fscore value: [0.81967213 0.79338843 0.848 0.85714286 0.85483871 0.78688525 0.82539683 0.75384615 0.81666667 0.87301587] mean value: 0.8228852892547043 key: train_fscore value: [0.87581699 0.8839779 0.8839779 0.88191882 0.88363636 0.87777778 0.87465181 0.88475836 0.86486486 0.85820896] mean value: 0.8769589750157328 key: test_precision value: [0.78125 0.76190476 0.79104478 0.83606557 0.81538462 0.76190476 0.7761194 0.69014085 0.79032258 0.80882353] mean value: 0.7812960847196456 key: train_precision value: [0.86055046 0.85714286 0.85714286 0.85663082 0.84521739 0.85405405 0.85326087 0.86388385 0.84826325 0.84249084] mean value: 0.8538637256451788 key: test_recall value: [0.86206897 0.82758621 0.9137931 0.87931034 0.89830508 0.81355932 0.88135593 0.83050847 0.84482759 0.94827586] mean value: 0.8699590882524839 key: train_recall value: [0.89163498 0.91254753 0.91254753 0.90874525 0.92571429 0.90285714 0.89714286 0.90666667 0.88212928 0.87452471] mean value: 0.9014510229947492 key: test_accuracy value: [0.81196581 0.78632479 0.83760684 0.85470085 0.84615385 0.77777778 0.81196581 0.72649573 0.81034483 0.86206897] mean value: 0.8125405246094901 key: train_accuracy value: [0.87345385 0.88011418 0.88011418 0.87821123 0.87821123 0.87440533 0.8715509 0.88201713 0.8621673 0.85551331] mean value: 0.8735758629297466 key: test_roc_auc value: [0.81239041 0.78667446 0.83825248 0.85490941 0.84570427 0.77746932 0.81136762 0.72559906 0.81034483 0.86206897] mean value: 0.8124780829924021 key: train_roc_auc value: [0.87343654 0.88008329 0.88008329 0.87818215 0.87825638 0.87443237 0.87157523 0.88204056 0.8621673 0.85551331] mean value: 0.8735770414629732 key: test_jcc value: [0.69444444 0.65753425 0.73611111 0.75 0.74647887 0.64864865 0.7027027 0.60493827 0.69014085 0.77464789] mean value: 0.700564703072099 key: train_jcc value: [0.77906977 0.79207921 0.79207921 0.78877888 0.79153094 0.78217822 0.77722772 0.79333333 0.76190476 0.75163399] mean value: 0.7809816028556901 MCC on Blind test: 0.12 MCC on Training: 0.63 Running classifier: 13 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [3.3443594 4.50718689 3.27742815 3.69520998 4.4072504 3.36745691 4.62901616 3.95466232 3.84780478 4.65955901] mean value: 3.968993401527405 key: score_time value: [0.01277328 0.01287365 0.01279068 0.01321363 0.01303411 0.01297021 0.01269913 0.01285219 0.01372838 0.01333761] mean value: 0.013027286529541016 key: test_mcc value: [0.84165009 0.85651622 0.84935886 0.88681491 0.90210482 0.74079346 0.91782516 0.75670177 0.87038828 0.85518611] mean value: 0.8477339678834654 key: train_mcc value: [0.94264702 0.98676672 0.9524689 0.96447334 0.99053017 0.92657524 0.98301789 0.96250699 0.97003062 0.98490479] mean value: 0.9663921685207304 key: test_fscore value: [0.92063492 0.928 0.92561983 0.94308943 0.9516129 0.87407407 0.95934959 0.88372093 0.93548387 0.928 ] mean value: 0.924958555823609 key: train_fscore value: [0.97137581 0.99338999 0.97634816 0.98225957 0.99526066 0.96330275 0.99150142 0.98127341 0.98501873 0.99245283] mean value: 0.9832183321400422 key: test_precision value: [0.85294118 0.86567164 0.88888889 0.89230769 0.90769231 0.77631579 0.921875 0.81428571 0.87878788 0.86567164] mean value: 0.8664437731488844 key: train_precision value: [0.9443447 0.98686679 0.97175141 0.96513761 0.99056604 0.92920354 0.98314607 0.96500921 0.9704797 0.98501873] mean value: 0.9691523807089844 key: test_recall value: [1. 1. 0.96551724 1. 1. 1. 1. 0.96610169 1. 1. ] mean value: 0.9931618936294566 key: train_recall value: [1. 1. 0.98098859 1. 1. 1. 1. 0.99809524 1. 1. ] mean value: 0.9979083831251131 key: test_accuracy value: [0.91452991 0.92307692 0.92307692 0.94017094 0.94871795 0.85470085 0.95726496 0.87179487 0.93103448 0.92241379] mean value: 0.9186781609195404 key: train_accuracy value: [0.97050428 0.99333968 0.97621313 0.98192198 0.99524263 0.96194101 0.99143673 0.9809705 0.98479087 0.99239544] mean value: 0.9828756245183838 key: test_roc_auc value: [0.91525424 0.92372881 0.92343659 0.94067797 0.94827586 0.85344828 0.95689655 0.87098188 0.93103448 0.92241379] mean value: 0.9186148451198131 key: train_roc_auc value: [0.97047619 0.99333333 0.97620858 0.98190476 0.99524715 0.96197719 0.99144487 0.98098678 0.98479087 0.99239544] mean value: 0.982876516386022 key: test_jcc value: [0.85294118 0.86567164 0.86153846 0.89230769 0.90769231 0.77631579 0.921875 0.79166667 0.87878788 0.86567164] mean value: 0.8614468256519368 key: train_jcc value: [0.9443447 0.98686679 0.95378928 0.96513761 0.99056604 0.92920354 0.98314607 0.96323529 0.9704797 0.98501873] mean value: 0.9671787759787733 MCC on Blind test: 0.04 MCC on Training: 0.85 Running classifier: 14 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.01703215 0.01633334 0.0164454 0.01663232 0.01722145 0.01636553 0.01687264 0.01634645 0.01658726 0.0163393 ] mean value: 0.016617584228515624 key: score_time value: [0.01257467 0.01248145 0.01288366 0.0122788 0.01253796 0.01253772 0.01243663 0.01258135 0.01243162 0.01245928] mean value: 0.012520313262939453 key: test_mcc value: [0.5726396 0.41940391 0.43246402 0.31657411 0.36768207 0.43958498 0.42008262 0.40245701 0.53647994 0.48391079] mean value: 0.43912790499210114 key: train_mcc value: [0.40059829 0.44935833 0.46534765 0.48049423 0.42370261 0.47098487 0.45593155 0.48049611 0.45660395 0.46580051] mean value: 0.45493180820136203 key: test_fscore value: [0.7826087 0.69642857 0.73846154 0.66101695 0.69421488 0.7027027 0.70175439 0.69565217 0.75675676 0.75 ] mean value: 0.7179596650065301 key: train_fscore value: [0.70254958 0.71400394 0.73058485 0.74074074 0.71655753 0.73574144 0.73120301 0.74024738 0.72286822 0.73415326] mean value: 0.7268649959216111 key: test_precision value: [0.78947368 0.72222222 0.66666667 0.65 0.67741935 0.75 0.72727273 0.71428571 0.79245283 0.72580645] mean value: 0.7215599651298149 key: train_precision value: [0.69793621 0.74180328 0.73694391 0.74003795 0.70404412 0.73434535 0.72170686 0.73954373 0.73715415 0.7306968 ] mean value: 0.7284212354822154 key: test_recall value: [0.77586207 0.67241379 0.82758621 0.67241379 0.71186441 0.66101695 0.6779661 0.6779661 0.72413793 0.77586207] mean value: 0.7177089421390999 key: train_recall value: [0.70722433 0.68821293 0.7243346 0.74144487 0.72952381 0.73714286 0.74095238 0.74095238 0.70912548 0.73764259] mean value: 0.7256556219445953 key: test_accuracy value: [0.78632479 0.70940171 0.70940171 0.65811966 0.68376068 0.71794872 0.70940171 0.7008547 0.76724138 0.74137931] mean value: 0.7183834364868847 key: train_accuracy value: [0.70028544 0.72407231 0.73263559 0.74024738 0.71170314 0.73549001 0.72787821 0.74024738 0.72813688 0.73288973] mean value: 0.7273586083143702 key: test_roc_auc value: [0.78623612 0.70908825 0.71040327 0.65824079 0.68351841 0.71843951 0.70967271 0.70105202 0.76724138 0.74137931] mean value: 0.7185271770894215 key: train_roc_auc value: [0.70027883 0.72410646 0.73264349 0.74024624 0.71172008 0.73549158 0.72789064 0.74024805 0.72813688 0.73288973] mean value: 0.7273652000724244 key: test_jcc value: [0.64285714 0.53424658 0.58536585 0.49367089 0.53164557 0.54166667 0.54054054 0.53333333 0.60869565 0.6 ] mean value: 0.5612022220268801 key: train_jcc value: [0.54148472 0.55521472 0.5755287 0.58823529 0.55830904 0.58195489 0.5762963 0.58761329 0.5660091 0.5799701 ] mean value: 0.5710616158912032 MCC on Blind test: 0.27 MCC on Training: 0.44 Running classifier: 15 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.01679373 0.01678443 0.01661301 0.01674104 0.01666188 0.01675272 0.01683021 0.0167644 0.01722121 0.01685405] mean value: 0.016801667213439942 key: score_time value: [0.01235628 0.01240373 0.01242781 0.0124433 0.01252604 0.01247597 0.01242232 0.01239824 0.01247382 0.01240182] mean value: 0.01243293285369873 key: test_mcc value: [0.50468462 0.2136586 0.31653125 0.35077464 0.33637807 0.31657411 0.26557483 0.19876886 0.46725672 0.31034483] mean value: 0.32805465305349313 key: train_mcc value: [0.39113743 0.31600037 0.35324431 0.3556399 0.44640804 0.38545023 0.39204017 0.35048204 0.39546299 0.39742532] mean value: 0.37832908034632073 key: test_fscore value: [0.74336283 0.58928571 0.64285714 0.6779661 0.64864865 0.65517241 0.62608696 0.57657658 0.72072072 0.65517241] mean value: 0.6535849520750071 key: train_fscore value: [0.69289827 0.64356436 0.65728643 0.6673209 0.71884058 0.68852459 0.68379447 0.65936255 0.6959847 0.69548511] mean value: 0.6803061964176786 key: test_precision value: [0.76363636 0.61111111 0.66666667 0.66666667 0.69230769 0.66666667 0.64285714 0.61538462 0.75471698 0.65517241] mean value: 0.6735186320222104 key: train_precision value: [0.6996124 0.6714876 0.69722814 0.68965517 0.72941176 0.69726562 0.71047228 0.69102296 0.7 0.70291262] mean value: 0.6989068578644974 key: test_recall value: [0.72413793 0.56896552 0.62068966 0.68965517 0.61016949 0.6440678 0.61016949 0.54237288 0.68965517 0.65517241] mean value: 0.6355055523085914 key: train_recall value: [0.68631179 0.61787072 0.621673 0.64638783 0.70857143 0.68 0.65904762 0.63047619 0.69201521 0.68821293] mean value: 0.6630566720984972 key: test_accuracy value: [0.75213675 0.60683761 0.65811966 0.67521368 0.66666667 0.65811966 0.63247863 0.5982906 0.73275862 0.65517241] mean value: 0.6635794282346006 key: train_accuracy value: [0.69552807 0.65746908 0.6755471 0.67745005 0.72312084 0.69267364 0.69552807 0.67459562 0.69771863 0.6986692 ] mean value: 0.6888300297019315 key: test_roc_auc value: [0.75189947 0.60651666 0.65780245 0.67533606 0.66715371 0.65824079 0.63267095 0.59877265 0.73275862 0.65517241] mean value: 0.6636323787258912 key: train_roc_auc value: [0.69553685 0.65750679 0.67559841 0.67747963 0.72310701 0.6926616 0.69549339 0.67455368 0.69771863 0.6986692 ] mean value: 0.6888325185587544 key: test_jcc value: [0.5915493 0.41772152 0.47368421 0.51282051 0.48 0.48717949 0.4556962 0.40506329 0.56338028 0.48717949] mean value: 0.4874274287828819 key: train_jcc value: [0.53010279 0.47445255 0.48952096 0.50073638 0.56108597 0.525 0.51951952 0.49182764 0.53372434 0.53313697] mean value: 0.5159107115985166 MCC on Blind test: 0.16 MCC on Training: 0.33 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=12, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.03383684 0.02924085 0.02915549 0.02335429 0.02480316 0.03102589 0.0272634 0.03421831 0.02680683 0.04565096] mean value: 0.03053560256958008 key: score_time value: [0.01274228 0.01223421 0.01239896 0.012254 0.01316118 0.01234722 0.01243043 0.01235843 0.01232433 0.01944852] mean value: 0.013169956207275391 key: test_mcc value: [0.59330306 0.45371312 0.43344067 0.36985384 0.50070314 0.43246402 0.47398728 0.35553102 0.30714756 0.12285902] mean value: 0.40430027335195096 key: train_mcc value: [0.5729652 0.52466838 0.45038279 0.41564319 0.46093804 0.64227363 0.47828058 0.49070133 0.30772232 0.23766648] mean value: 0.4581241947304867 key: test_fscore value: [0.80916031 0.75362319 0.74666667 0.49382716 0.77272727 0.67307692 0.72072072 0.56521739 0.70731707 0.20588235] mean value: 0.6448219054850974 key: train_fscore value: [0.80203908 0.78369906 0.75626424 0.5648267 0.73944954 0.81268583 0.70981211 0.64981949 0.70746469 0.27009646] mean value: 0.6796157208792756 key: test_precision value: [0.7260274 0.65 0.60869565 0.86956522 0.69863014 0.77777778 0.76923077 0.78787879 0.54716981 0.7 ] mean value: 0.7134975550019883 key: train_precision value: [0.7250384 0.66666667 0.62958281 0.86956522 0.71327434 0.84710744 0.7852194 0.88235294 0.54734651 0.875 ] mean value: 0.7541153722151843 key: test_recall value: [0.9137931 0.89655172 0.96551724 0.34482759 0.86440678 0.59322034 0.6779661 0.44067797 1. 0.12068966] mean value: 0.6817650496785506 key: train_recall value: [0.8973384 0.95057034 0.94676806 0.41825095 0.76761905 0.78095238 0.64761905 0.51428571 1. 0.15969582] mean value: 0.7083099764620677 key: test_accuracy value: [0.78632479 0.70940171 0.67521368 0.64957265 0.74358974 0.70940171 0.73504274 0.65811966 0.5862069 0.53448276] mean value: 0.6787356321839081 key: train_accuracy value: [0.77830637 0.73739296 0.69457659 0.67745005 0.72978116 0.82017127 0.73549001 0.72312084 0.5865019 0.56844106] mean value: 0.7051232214114386 key: test_roc_auc value: [0.78740503 0.71098773 0.67767387 0.64699006 0.74254822 0.71040327 0.73553477 0.65999416 0.5862069 0.53448276] mean value: 0.6792226767971946 key: train_roc_auc value: [0.77819301 0.73718993 0.69433641 0.6776969 0.72981713 0.82013399 0.73540648 0.72292232 0.5865019 0.56844106] mean value: 0.7050639145391996 key: test_jcc value: [0.67948718 0.60465116 0.59574468 0.32786885 0.62962963 0.50724638 0.56338028 0.39393939 0.54716981 0.1147541 ] mean value: 0.4963871467340127 key: train_jcc value: [0.66950355 0.6443299 0.60805861 0.39355993 0.58660844 0.68447412 0.55016181 0.48128342 0.54734651 0.15613383] mean value: 0.5321460123353413 MCC on Blind test: 0.18 MCC on Training: 0.4 Running classifier: 17 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.03983068 0.04122448 0.0430038 0.041219 0.04250479 0.0415411 0.04094839 0.04331493 0.04037523 0.04264736] mean value: 0.04166097640991211 key: score_time value: [0.01337886 0.01333261 0.01319027 0.01360321 0.03049636 0.01328182 0.01333094 0.01321149 0.01344585 0.01902866] mean value: 0.015630006790161133 key: test_mcc value: [0.88681491 0.94998574 0.87156767 0.87156767 0.87128374 0.8865947 0.90210482 0.87128374 0.85518611 0.88578518] mean value: 0.8852174272337898 key: train_mcc value: [0.87626842 0.91246552 0.88820876 0.89164276 0.89856116 0.9089842 0.87969529 0.91248013 0.90383944 0.88319525] mean value: 0.89553409341171 key: test_fscore value: [0.94308943 0.97478992 0.93548387 0.93548387 0.93650794 0.944 0.9516129 0.93650794 0.928 0.94308943] mean value: 0.9428565295932169 key: train_fscore value: [0.93844781 0.95636364 0.9443447 0.94604317 0.94936709 0.95454545 0.94001791 0.95628415 0.9520362 0.94180842] mean value: 0.9479258535807722 key: test_precision value: [0.89230769 0.95081967 0.87878788 0.87878788 0.88059701 0.89393939 0.90769231 0.88059701 0.86567164 0.89230769] mean value: 0.8921508187595784 key: train_precision value: [0.88403361 0.91637631 0.89455782 0.89761092 0.90361446 0.91304348 0.88682432 0.91623037 0.90846287 0.89001692] mean value: 0.9010771079091076 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.94017094 0.97435897 0.93162393 0.93162393 0.93162393 0.94017094 0.94871795 0.93162393 0.92241379 0.93965517] mean value: 0.9391983495431772 key: train_accuracy value: [0.93434824 0.95432921 0.94100856 0.94291151 0.94671741 0.95242626 0.93625119 0.95432921 0.94961977 0.93821293] mean value: 0.9450154298097411 key: test_roc_auc value: [0.94067797 0.97457627 0.93220339 0.93220339 0.93103448 0.93965517 0.94827586 0.93103448 0.92241379 0.93965517] mean value: 0.9391729982466395 key: train_roc_auc value: [0.93428571 0.95428571 0.94095238 0.94285714 0.94676806 0.95247148 0.93631179 0.95437262 0.94961977 0.93821293] mean value: 0.9450137606373348 key: test_jcc value: [0.89230769 0.95081967 0.87878788 0.87878788 0.88059701 0.89393939 0.90769231 0.88059701 0.86567164 0.89230769] mean value: 0.8921508187595784 key: train_jcc value: [0.88403361 0.91637631 0.89455782 0.89761092 0.90361446 0.91304348 0.88682432 0.91623037 0.90846287 0.89001692] mean value: 0.9010771079091076 MCC on Blind test: -0.13 MCC on Training: 0.89 Running classifier: 18 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42))]) key: fit_time value: [0.86369729 0.91857672 0.80703855 0.83885241 0.84991646 0.89559031 0.87976933 0.84424949 0.84226823 0.83772945] mean value: 0.8577688217163086 key: score_time value: [0.17894626 0.20926094 0.15233183 0.14892697 0.17515779 0.19063354 0.21553993 0.17763996 0.17254782 0.20998645] mean value: 0.18309714794158935 key: test_mcc value: [0.96638414 0.93384219 0.96638414 0.96638414 0.96636481 0.96636481 0.98304594 0.94994292 0.98290472 0.96609178] mean value: 0.964770957915231 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.98305085 0.96666667 0.98305085 0.98305085 0.98333333 0.98333333 0.99159664 0.97520661 0.99145299 0.98305085] mean value: 0.9823792964842543 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.96666667 0.93548387 0.96666667 0.96666667 0.96721311 0.96721311 0.98333333 0.9516129 0.98305085 0.96666667] mean value: 0.9654573851159372 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.98290598 0.96581197 0.98290598 0.98290598 0.98290598 0.98290598 0.99145299 0.97435897 0.99137931 0.98275862] mean value: 0.9820291777188329 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.98305085 0.96610169 0.98305085 0.98305085 0.98275862 0.98275862 0.99137931 0.97413793 0.99137931 0.98275862] mean value: 0.9820426651081238 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.96666667 0.93548387 0.96666667 0.96666667 0.96721311 0.96721311 0.98333333 0.9516129 0.98305085 0.96666667] mean value: 0.9654573851159372 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.3 MCC on Training: 0.96 Running classifier: 19 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea...age_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42))]) key: fit_time value: [1.38455534 1.37864733 1.22167349 1.30405855 1.29308391 1.23465395 1.24610782 1.26173639 1.31113195 1.2294755 ] mean value: 1.2865124225616456 key: score_time value: [0.1673522 0.27111268 0.26433134 0.26110101 0.19302273 0.23650408 0.26260781 0.28210044 0.24962091 0.19665933] mean value: 0.23844125270843505 key: test_mcc value: [0.94998574 0.88364975 0.8524126 0.94998574 0.9337672 0.86770252 0.94884541 0.84914237 0.93325653 0.88578518] mean value: 0.905453303061526 key: train_mcc value: [0.98864661 0.98669535 0.99048702 0.99241689 0.992417 0.99238819 0.98480503 0.98672413 0.99242413 0.99431275] mean value: 0.9901317105464811 key: test_fscore value: [0.97478992 0.94214876 0.92682927 0.97478992 0.96721311 0.93548387 0.97478992 0.92682927 0.96666667 0.94308943] mean value: 0.9532630128097921 key: train_fscore value: [0.99432892 0.99336493 0.99525166 0.99621212 0.99620493 0.99619048 0.99240987 0.99336493 0.99621212 0.9971564 ] mean value: 0.9950696359711504 key: test_precision value: [0.95081967 0.9047619 0.87692308 0.95081967 0.93650794 0.89230769 0.96666667 0.890625 0.93548387 0.89230769] mean value: 0.9197223184705008 key: train_precision value: [0.9887218 0.9905482 0.9943074 0.99245283 0.99243856 0.99619048 0.98865784 0.98867925 0.99245283 0.99432892] mean value: 0.9918778121713283 key: test_recall value: [1. 0.98275862 0.98275862 1. 1. 0.98305085 0.98305085 0.96610169 1. 1. ] mean value: 0.9897720631209819 key: train_recall value: [1. 0.99619772 0.99619772 1. 1. 0.99619048 0.99619048 0.99809524 1. 1. ] mean value: 0.9982871627738547 key: test_accuracy value: [0.97435897 0.94017094 0.92307692 0.97435897 0.96581197 0.93162393 0.97435897 0.92307692 0.96551724 0.93965517] mean value: 0.951201002063071 key: train_accuracy value: [0.99429115 0.99333968 0.99524263 0.9961941 0.9961941 0.9961941 0.9923882 0.99333968 0.99619772 0.99714829] mean value: 0.9950529642238244 key: test_roc_auc value: [0.97457627 0.94053185 0.9235827 0.97457627 0.96551724 0.9311806 0.97428404 0.92270602 0.96551724 0.93965517] mean value: 0.9512127410870835 key: train_roc_auc value: [0.99428571 0.99333695 0.99524172 0.99619048 0.99619772 0.9961941 0.99239182 0.9933442 0.99619772 0.99714829] mean value: 0.9950528698171283 key: test_jcc value: [0.95081967 0.890625 0.86363636 0.95081967 0.93650794 0.87878788 0.95081967 0.86363636 0.93548387 0.89230769] mean value: 0.911344412223742 key: train_jcc value: [0.9887218 0.98681733 0.9905482 0.99245283 0.99243856 0.99240987 0.98493409 0.98681733 0.99245283 0.99432892] mean value: 0.9901921760272163 MCC on Blind test: 0.28 MCC on Training: 0.91 Running classifier: 20 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.04506922 0.04264402 0.03748655 0.04166341 0.04430318 0.04000258 0.04637957 0.06044912 0.04191494 0.03240514] mean value: 0.043231773376464847 key: score_time value: [0.01908255 0.0293324 0.02032328 0.02935433 0.02302575 0.01906061 0.02652812 0.01906419 0.0296011 0.02922177] mean value: 0.024459409713745116 key: test_mcc value: [0.57355974 0.47215912 0.61462423 0.69408772 0.62415935 0.52227657 0.55552309 0.37107233 0.53519917 0.62217102] mean value: 0.5584832332913832 key: train_mcc value: [0.70277841 0.70977178 0.71822399 0.70125279 0.69501474 0.72026795 0.66928999 0.70942617 0.68110089 0.67517954] mean value: 0.6982306232948083 key: test_fscore value: [0.78991597 0.74380165 0.816 0.85 0.81666667 0.7704918 0.77966102 0.70866142 0.77310924 0.81666667] mean value: 0.7864974433860603 key: train_fscore value: [0.85556578 0.85820204 0.86317723 0.85636856 0.85133887 0.86397059 0.83707865 0.85714286 0.84328358 0.83973758] mean value: 0.8525865741451322 key: test_precision value: [0.7704918 0.71428571 0.76119403 0.82258065 0.80327869 0.74603175 0.77966102 0.66176471 0.75409836 0.79032258] mean value: 0.7603709291265178 key: train_precision value: [0.82887701 0.83725136 0.8348135 0.81583477 0.82616487 0.8348135 0.82320442 0.84065934 0.82783883 0.82809612] mean value: 0.8297553708691178 key: test_recall value: [0.81034483 0.77586207 0.87931034 0.87931034 0.83050847 0.79661017 0.77966102 0.76271186 0.79310345 0.84482759] mean value: 0.8152250146113383 key: train_recall value: [0.88403042 0.88022814 0.89353612 0.90114068 0.87809524 0.8952381 0.85142857 0.87428571 0.85931559 0.85171103] mean value: 0.8769009596233932 key: test_accuracy value: [0.78632479 0.73504274 0.8034188 0.84615385 0.81196581 0.76068376 0.77777778 0.68376068 0.76724138 0.81034483] mean value: 0.7782714412024756 key: train_accuracy value: [0.85061846 0.85442436 0.85823026 0.84871551 0.84681256 0.85918173 0.83444339 0.85442436 0.84030418 0.83745247] mean value: 0.8484607272451006 key: test_roc_auc value: [0.78652835 0.73538866 0.80406195 0.84643483 0.81180596 0.76037405 0.77776154 0.68308007 0.76724138 0.81034483] mean value: 0.7783021624780831 key: train_roc_auc value: [0.85058664 0.85439978 0.85819663 0.84866558 0.8468423 0.85921601 0.83445953 0.85444324 0.84030418 0.83745247] mean value: 0.8484566358862937 key: test_jcc value: [0.65277778 0.59210526 0.68918919 0.73913043 0.69014085 0.62666667 0.63888889 0.54878049 0.63013699 0.69014085] mean value: 0.6497957384710119 key: train_jcc value: [0.74758842 0.75162338 0.75928918 0.74881517 0.74115756 0.7605178 0.71980676 0.75 0.72903226 0.72374798] mean value: 0.7431578500614202 MCC on Blind test: 0.21 MCC on Training: 0.56 Running classifier: 21 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=3))]) key: fit_time value: [0.15391564 0.14219046 0.13279486 0.15582824 0.16229796 0.15176105 0.16613412 0.14687538 0.19137168 0.18942475] mean value: 0.15925941467285157 key: score_time value: [0.02239823 0.019238 0.01925468 0.02264333 0.02337599 0.03767323 0.02545118 0.0210669 0.02637291 0.02336454] mean value: 0.024083900451660156 key: test_mcc value: [0.59577749 0.59577749 0.63025127 0.76118097 0.65944011 0.58971362 0.48927719 0.37107233 0.53519917 0.65673607] mean value: 0.5884425719785422 key: train_mcc value: [0.72003666 0.75549627 0.73549424 0.7339336 0.72271113 0.72687805 0.70585505 0.70942617 0.68110089 0.70023251] mean value: 0.719116457339193 key: test_fscore value: [0.80645161 0.80645161 0.82258065 0.88135593 0.83606557 0.79661017 0.75806452 0.70866142 0.77310924 0.83333333] mean value: 0.8022684056915829 key: train_fscore value: [0.86397059 0.88160291 0.87155963 0.87184116 0.86550778 0.866171 0.85581395 0.85714286 0.84328358 0.85288641] mean value: 0.8629779870045768 key: test_precision value: [0.75757576 0.75757576 0.77272727 0.86666667 0.80952381 0.79661017 0.72307692 0.66176471 0.75409836 0.80645161] mean value: 0.770607103607903 key: train_precision value: [0.83629893 0.84615385 0.84219858 0.82989691 0.83274648 0.84573503 0.83636364 0.84065934 0.82783883 0.83576642] mean value: 0.8373658001630904 key: test_recall value: [0.86206897 0.86206897 0.87931034 0.89655172 0.86440678 0.79661017 0.79661017 0.76271186 0.79310345 0.86206897] mean value: 0.8375511396843951 key: train_recall value: [0.89353612 0.92015209 0.90304183 0.91825095 0.90095238 0.88761905 0.87619048 0.87428571 0.85931559 0.87072243] mean value: 0.8904066630454462 key: test_accuracy value: [0.79487179 0.79487179 0.81196581 0.88034188 0.82905983 0.79487179 0.74358974 0.68376068 0.76724138 0.82758621] mean value: 0.792816091954023 key: train_accuracy value: [0.85918173 0.87630828 0.86679353 0.86489058 0.86013321 0.86298763 0.85252141 0.85442436 0.84030418 0.84980989] mean value: 0.8587354791561903 key: test_roc_auc value: [0.79544126 0.79544126 0.81253653 0.88047925 0.82875511 0.79485681 0.74313267 0.68308007 0.76724138 0.82758621] mean value: 0.792855055523086 key: train_roc_auc value: [0.85914901 0.87626652 0.86675901 0.86483976 0.86017201 0.86301104 0.85254391 0.85444324 0.84030418 0.84980989] mean value: 0.8587298569617963 key: test_jcc value: [0.67567568 0.67567568 0.69863014 0.78787879 0.71830986 0.66197183 0.61038961 0.54878049 0.63013699 0.71428571] mean value: 0.6721734765138858 key: train_jcc value: [0.7605178 0.78827362 0.77235772 0.7728 0.76290323 0.76393443 0.74796748 0.75 0.72903226 0.74350649] mean value: 0.7591293021846932 MCC on Blind test: 0.11 MCC on Training: 0.59 Running classifier: 22 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.09125042 0.06253314 0.06444263 0.06769347 0.06363511 0.06245351 0.06400967 0.06244755 0.06347847 0.0705626 ] mean value: 0.067250657081604 key: score_time value: [0.02429032 0.02417755 0.02536464 0.0247426 0.02456594 0.02387786 0.02976847 0.0239172 0.02501202 0.02479982] mean value: 0.025051641464233398 key: test_mcc value: [0.76118097 0.50423855 0.71829222 0.6087526 0.72833836 0.55597781 0.72698045 0.43600234 0.63802587 0.65556228] mean value: 0.6333351449627127 key: train_mcc value: [0.70696386 0.71276357 0.72606312 0.7107514 0.71458007 0.71463599 0.72063476 0.74164223 0.71863637 0.70547748] mean value: 0.717214885991557 key: test_fscore value: [0.88135593 0.74782609 0.864 0.79279279 0.85964912 0.77586207 0.86206897 0.71794872 0.82051282 0.83050847] mean value: 0.815252498228029 key: train_fscore value: [0.85416667 0.85522531 0.86206897 0.85551331 0.85768501 0.85604607 0.85769603 0.87265918 0.85904762 0.85110471] mean value: 0.8581212859584436 key: test_precision value: [0.86666667 0.75438596 0.80597015 0.83018868 0.89090909 0.78947368 0.87719298 0.72413793 0.81355932 0.81666667] mean value: 0.8169151137388766 key: train_precision value: [0.8509434 0.86266925 0.86872587 0.85551331 0.85444234 0.86266925 0.87204724 0.85819521 0.86068702 0.86019417] mean value: 0.8606087061817288 key: test_recall value: [0.89655172 0.74137931 0.93103448 0.75862069 0.83050847 0.76271186 0.84745763 0.71186441 0.82758621 0.84482759] mean value: 0.8152542372881355 key: train_recall value: [0.85741445 0.84790875 0.85551331 0.85551331 0.86095238 0.84952381 0.84380952 0.88761905 0.85741445 0.84220532] mean value: 0.8557874343653811 key: test_accuracy value: [0.88034188 0.75213675 0.85470085 0.8034188 0.86324786 0.77777778 0.86324786 0.71794872 0.81896552 0.82758621] mean value: 0.8159372236958443 key: train_accuracy value: [0.85347288 0.85632731 0.86298763 0.85537583 0.85727878 0.85727878 0.86013321 0.87059943 0.85931559 0.8526616 ] mean value: 0.858543103978467 key: test_roc_auc value: [0.88047925 0.75204559 0.85534775 0.80303916 0.8635301 0.77790766 0.86338399 0.71800117 0.81896552 0.82758621] mean value: 0.8160286382232613 key: train_roc_auc value: [0.85346913 0.85633533 0.86299475 0.8553757 0.85728227 0.85727141 0.86011769 0.87061561 0.85931559 0.8526616 ] mean value: 0.858543907296759 key: test_jcc value: [0.78787879 0.59722222 0.76056338 0.65671642 0.75384615 0.63380282 0.75757576 0.56 0.69565217 0.71014493] mean value: 0.6913402638065744 key: train_jcc value: [0.74545455 0.74706868 0.75757576 0.74750831 0.75083056 0.74832215 0.75084746 0.77408638 0.75292154 0.74080268] mean value: 0.7515418045673221 MCC on Blind test: 0.24 MCC on Training: 0.63 Running classifier: 23 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.03605509 0.04380512 0.04021358 0.04026723 0.04069591 0.04130888 0.03155184 0.04442549 0.03761387 0.04906821] mean value: 0.040500521659851074 key: score_time value: [0.01061249 0.01231194 0.01249242 0.01243424 0.01236629 0.0122304 0.01236701 0.01243854 0.01223993 0.01244855] mean value: 0.012194180488586425 key: test_mcc value: [0.64826603 0.50748401 0.55654161 0.49044237 0.43212006 0.49116775 0.47906943 0.36286116 0.45479403 0.41808123] mean value: 0.48408276611715406 key: train_mcc value: [0.61891532 0.61958487 0.52447446 0.53281146 0.57520138 0.67170155 0.52787488 0.46160262 0.37621027 0.46349888] mean value: 0.53718756873437 key: test_fscore value: [0.80373832 0.77464789 0.79166667 0.76315789 0.63917526 0.76190476 0.62222222 0.55555556 0.57142857 0.57471264] mean value: 0.6858209779005692 key: train_fscore value: [0.78406709 0.82171799 0.78227655 0.78479087 0.74891775 0.8441331 0.69505178 0.59411012 0.48827586 0.61 ] mean value: 0.7153341107444386 key: test_precision value: [0.87755102 0.6547619 0.6627907 0.61702128 0.81578947 0.71641791 0.90322581 0.80645161 0.92307692 0.86206897] mean value: 0.7839155591521206 key: train_precision value: [0.87383178 0.71610169 0.65389527 0.6539924 0.86716792 0.78119935 0.87790698 0.90625 0.88944724 0.89051095] mean value: 0.8110303573969864 key: test_recall value: [0.74137931 0.94827586 0.98275862 1. 0.52542373 0.81355932 0.47457627 0.42372881 0.4137931 0.43103448] mean value: 0.6754529514903566 key: train_recall value: [0.71102662 0.96387833 0.97338403 0.98098859 0.65904762 0.91809524 0.5752381 0.44190476 0.3365019 0.46387833] mean value: 0.702394350896252 key: test_accuracy value: [0.82051282 0.72649573 0.74358974 0.69230769 0.7008547 0.74358974 0.70940171 0.65811966 0.68965517 0.68103448] mean value: 0.7165561450044209 key: train_accuracy value: [0.80399619 0.79067555 0.72882969 0.73073264 0.77925785 0.83063749 0.74785918 0.69838249 0.6473384 0.70342205] mean value: 0.7461131531440273 key: test_roc_auc value: [0.8198422 0.72837522 0.7456166 0.69491525 0.70236704 0.74298656 0.71142607 0.66014027 0.68965517 0.68103448] mean value: 0.717635885447107 key: train_roc_auc value: [0.80408474 0.79051059 0.72859678 0.7304943 0.77914358 0.83072062 0.74769509 0.69813869 0.6473384 0.70342205] mean value: 0.7460144848814051 key: test_jcc value: [0.671875 0.63218391 0.65517241 0.61702128 0.46969697 0.61538462 0.4516129 0.38461538 0.4 0.40322581] mean value: 0.5300788277809214 key: train_jcc value: [0.64482759 0.69738652 0.64240903 0.64580726 0.59861592 0.73030303 0.53262787 0.42258652 0.3229927 0.43884892] mean value: 0.5676405354862417 MCC on Blind test: 0.26 MCC on Training: 0.48 Running classifier: 24 Model_name: XGBoost Model func: /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:463: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_CV['source_data'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:490: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_BT['source_data'] = 'BT' XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.23089981 0.1947422 0.18003774 0.18680644 0.17727733 0.18047881 0.19937158 0.17601323 0.32074976 0.19159031] mean value: 0.20379672050476075 key: score_time value: [0.01255655 0.01429009 0.01142097 0.01144838 0.01187515 0.01235986 0.01142073 0.01222897 0.01209998 0.01240349] mean value: 0.012210416793823241 key: test_mcc value: [0.96638414 0.93384219 0.94998574 0.96638414 0.91782516 0.8865947 0.98304594 0.90210482 0.96609178 0.90138782] mean value: 0.9373646418989402 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.98305085 0.96666667 0.97478992 0.98305085 0.95934959 0.944 0.99159664 0.9516129 0.98305085 0.95081967] mean value: 0.9687987932514286 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.96666667 0.93548387 0.95081967 0.96666667 0.921875 0.89393939 0.98333333 0.90769231 0.96666667 0.90625 ] mean value: 0.9399393578063924 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.98290598 0.96581197 0.97435897 0.98290598 0.95726496 0.94017094 0.99145299 0.94871795 0.98275862 0.94827586] mean value: 0.9674624226348364 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.98305085 0.96610169 0.97457627 0.98305085 0.95689655 0.93965517 0.99137931 0.94827586 0.98275862 0.94827586] mean value: 0.9674021040327295 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.96666667 0.93548387 0.95081967 0.96666667 0.921875 0.89393939 0.98333333 0.90769231 0.96666667 0.90625 ] mean value: 0.9399393578063924 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.27 MCC on Training: 0.94 Extracting tts_split_name: 80_20 Total cols in each df: CV df: 8 metaDF: 17 Adding column: Model_name Total cols in bts df: BT_df: 8 First proceeding to rowbind CV and BT dfs: Final output should have: 25 columns Combinig 2 using pd.concat by row ~ rowbind Checking Dims of df to combine: Dim of CV: (24, 8) Dim of BT: (24, 8) 8 Number of Common columns: 8 These are: ['source_data', 'Precision', 'JCC', 'MCC', 'Recall', 'Accuracy', 'F1', 'ROC_AUC'] Concatenating dfs with different resampling methods [WF]: Split type: 80_20 No. of dfs combining: 2 PASS: 2 dfs successfully combined nrows in combined_df_wf: 48 ncols in combined_df_wf: 8 PASS: proceeding to merge metadata with CV and BT dfs Adding column: Model_name ========================================================= SUCCESS: Ran multiple classifiers ======================================================= ============================================================== Running several classification models (n): 24 List of models: ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('Gaussian NB', GaussianNB()) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('K-Nearest Neighbors', KNeighborsClassifier()) ('LDA', LinearDiscriminantAnalysis()) ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(cv=3, random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Multinomial', MultinomialNB()) ('Naive Bayes', BernoulliNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=12, random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Random Forest', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42)) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=3)) ('SVC', SVC(random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=12, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ================================================================ Running classifier: 1 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... ÿKt”bK…”ŒC”t”R”.”…”R”Kdt”}”(h:KŒ check_input”ˆu‡”ahŒThreadingBackend”“”)”}”(Œ nesting_level”KŒinner_max_num_threads”NubN†”N}”t”R”sbŒargs”)Œkwargs”}”Œ loky_pickler”Œ cloudpickle”uBuilding estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... @,@Y@ a@@S@`a@O@`a@1@@1@@€F@ a@"@B@ a@:@`Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.5s remaining: 1.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.5s remaining: 1.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.6s remaining: 0.2s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.6s remaining: 1.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.6s remaining: 0.2s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.6s remaining: 1.2s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.6s remaining: 1.2s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.6s remaining: 1.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.6s remaining: 1.2s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.6s finished Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.6s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.6s remaining: 1.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.6s remaining: 0.2s Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.6s remaining: 0.2s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.6s remaining: 1.3s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.6s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.6s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.6s remaining: 0.2s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.6s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.7s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.6s remaining: 0.2s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.6s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.7s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.7s remaining: 0.2s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.7s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.7s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.7s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.7s remaining: 1.3s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.7s remaining: 0.2s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.7s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.11309505 0.10626292 0.10851073 0.11252499 0.11290431 0.11244988 0.11491919 0.1190002 0.11336207 0.11038923] mean value: 0.11234185695648194 key: score_time value: [0.01440907 0.01458335 0.01522136 0.01546192 0.01535463 0.01591706 0.01550293 0.0156796 0.01551437 0.01463032] mean value: 0.015227460861206054 key: test_mcc value: [ 0.14545455 0.45226702 -0.06741999 0.33636364 0.6 0.30151134 0.10050378 0.61237244 0.10050378 0.30151134] mean value: 0.2883067900341667 key: train_mcc value: [0.97814142 1. 1. 0.98913043 1. 1. 1. 0.98918887 1. 1. ] mean value: 0.9956460730155297 key: test_fscore value: [0.57142857 0.625 0.56 0.66666667 0.8 0.63157895 0.52631579 0.81818182 0.52631579 0.63157895] mean value: 0.6357066529961266 key: train_fscore value: [0.98913043 1. 1. 0.99453552 1. 1. 1. 0.99453552 1. 1. ] mean value: 0.9978201473033975 key: test_precision value: [0.54545455 0.83333333 0.5 0.7 0.8 0.66666667 0.55555556 0.75 0.55555556 0.66666667] mean value: 0.6573232323232323 key: train_precision value: [0.98913043 1. 1. 0.98913043 1. 1. 1. 1. 1. 1. ] mean value: 0.9978260869565216 key: test_recall value: [0.6 0.5 0.63636364 0.63636364 0.8 0.6 0.5 0.9 0.5 0.6 ] mean value: 0.6272727272727272 key: train_recall value: [0.98913043 1. 1. 1. 1. 1. 1. 0.98913043 1. 1. ] mean value: 0.9978260869565216 key: test_accuracy value: [0.57142857 0.71428571 0.47619048 0.66666667 0.8 0.65 0.55 0.8 0.55 0.65 ] mean value: 0.6428571428571429 key: train_accuracy value: [0.98907104 1. 1. 0.99453552 1. 1. 1. 0.99456522 1. 1. ] mean value: 0.9978171774768354 key: test_roc_auc value: [0.57272727 0.70454545 0.46818182 0.66818182 0.8 0.65 0.55 0.8 0.55 0.65 ] mean value: 0.6413636363636362 key: train_roc_auc value: [0.98907071 1. 1. 0.99456522 1. 1. 1. 0.99456522 1. 1. ] mean value: 0.9978201146679406 key: test_jcc value: [0.4 0.45454545 0.38888889 0.5 0.66666667 0.46153846 0.35714286 0.69230769 0.35714286 0.46153846] mean value: 0.47397713397713404 key: train_jcc value: [0.97849462 1. 1. 0.98913043 1. 1. 1. 0.98913043 1. 1. ] mean value: 0.9956755493221131 MCC on Blind test: 0.22 MCC on Training: 0.29 Running classifier: 2 Model_name: Bagging Classifier Model func: BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3))]) key: fit_time value: [0.15898657 0.18831754 0.17267036 0.17799735 0.18473816 0.19180965 0.17803383 0.17457032 0.1688962 0.18733478] mean value: 0.17833547592163085 key: score_time value: [0.06766033 0.0649991 0.07507944 0.06854892 0.03954482 0.05604243 0.04121327 0.04158092 0.07036114 0.03695416] mean value: 0.05619845390319824 key: test_mcc value: [ 0.33636364 0.71562645 -0.13762047 0.74795759 0.14002801 0.31448545 0.43643578 0.70352647 0.4 0.50251891] mean value: 0.41593218232641566 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.66666667 0.84210526 0.4 0.84210526 0.66666667 0.58823529 0.625 0.84210526 0.7 0.73684211] mean value: 0.6909726522187822 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.63636364 0.88888889 0.44444444 1. 0.52941176 0.71428571 0.83333333 0.88888889 0.7 0.77777778] mean value: 0.7413394448688566 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.7 0.8 0.36363636 0.72727273 0.9 0.5 0.5 0.8 0.7 0.7 ] mean value: 0.6690909090909092 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.66666667 0.85714286 0.42857143 0.85714286 0.55 0.65 0.7 0.85 0.7 0.75 ] mean value: 0.700952380952381 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.66818182 0.85454545 0.43181818 0.86363636 0.55 0.65 0.7 0.85 0.7 0.75 ] mean value: 0.7018181818181819 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.5 0.72727273 0.25 0.72727273 0.5 0.41666667 0.45454545 0.72727273 0.53846154 0.58333333] mean value: 0.5424825174825174 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.15 MCC on Training: 0.42 Running classifier: 3 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.01798463 0.01625085 0.01624799 0.01608205 0.01813674 0.01586342 0.01570415 0.01801682 0.01612949 0.01621652] mean value: 0.016663265228271485 key: score_time value: [0.01005077 0.00951958 0.0095346 0.00946045 0.00886917 0.00951648 0.00950456 0.00980687 0.0095396 0.00933504] mean value: 0.00951371192932129 key: test_mcc value: [ 0.13483997 0.42727273 -0.05504819 0.26967994 0.11547005 0.2 0.30151134 0.20412415 0.2 0.20412415] mean value: 0.2001974145373906 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.47058824 0.7 0.52173913 0.55555556 0.64 0.6 0.63157895 0.63636364 0.6 0.63636364] mean value: 0.5992189141380149 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.57142857 0.7 0.5 0.71428571 0.53333333 0.6 0.66666667 0.58333333 0.6 0.58333333] mean value: 0.6052380952380951 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.4 0.7 0.54545455 0.45454545 0.8 0.6 0.6 0.7 0.6 0.7 ] mean value: 0.61 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.57142857 0.71428571 0.47619048 0.61904762 0.55 0.6 0.65 0.6 0.6 0.6 ] mean value: 0.598095238095238 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.56363636 0.71363636 0.47272727 0.62727273 0.55 0.6 0.65 0.6 0.6 0.6 ] mean value: 0.5977272727272727 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.30769231 0.53846154 0.35294118 0.38461538 0.47058824 0.42857143 0.46153846 0.46666667 0.42857143 0.46666667] mean value: 0.4306313294548588 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.24 MCC on Training: 0.2 Running classifier: 4 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.00898123 0.00904846 0.00880647 0.00882173 0.00896263 0.008991 0.00913119 0.00955939 0.00990725 0.0089457 ] mean value: 0.009115505218505859 key: score_time value: [0.00847816 0.00852728 0.00852227 0.00853825 0.00847077 0.00881791 0.00925326 0.00866628 0.00881672 0.00858212] mean value: 0.008667302131652833 key: test_mcc value: [0.35527986 0.35527986 0.13762047 0.38924947 0.34641016 0.10050378 0.10050378 0.31448545 0.10050378 0.30151134] mean value: 0.25013479568138625 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.69565217 0.69565217 0.60869565 0.42857143 0.72 0.52631579 0.52631579 0.69565217 0.52631579 0.66666667] mean value: 0.6089837637572192 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.61538462 0.61538462 0.58333333 1. 0.6 0.55555556 0.55555556 0.61538462 0.55555556 0.63636364] mean value: 0.6332517482517483 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.8 0.8 0.63636364 0.27272727 0.9 0.5 0.5 0.8 0.5 0.7 ] mean value: 0.6409090909090909 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.66666667 0.66666667 0.57142857 0.61904762 0.65 0.55 0.55 0.65 0.55 0.65 ] mean value: 0.6123809523809525 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.67272727 0.67272727 0.56818182 0.63636364 0.65 0.55 0.55 0.65 0.55 0.65 ] mean value: 0.615 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.53333333 0.53333333 0.4375 0.27272727 0.5625 0.35714286 0.35714286 0.53333333 0.35714286 0.5 ] mean value: 0.44441558441558443 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.18 MCC on Training: 0.25 Running classifier: 5 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.09650922 0.09756088 0.0984416 0.10114217 0.10171032 0.10099244 0.09798741 0.10180497 0.10557342 0.10577893] mean value: 0.10075013637542725 key: score_time value: [0.01804614 0.0179863 0.01846957 0.01874781 0.01786375 0.01869702 0.01905489 0.01695824 0.01866364 0.01848102] mean value: 0.01829683780670166 key: test_mcc value: [ 0.52727273 0.61818182 -0.23636364 0.52295779 0.52414242 0.40824829 0.2 0.4 0.2 0.6 ] mean value: 0.3764439406350753 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.76190476 0.8 0.38095238 0.7826087 0.7826087 0.66666667 0.6 0.7 0.6 0.8 ] mean value: 0.6874741200828157 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.72727273 0.8 0.4 0.75 0.69230769 0.75 0.6 0.7 0.6 0.8 ] mean value: 0.681958041958042 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.8 0.8 0.36363636 0.81818182 0.9 0.6 0.6 0.7 0.6 0.8 ] mean value: 0.6981818181818181 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.76190476 0.80952381 0.38095238 0.76190476 0.75 0.7 0.6 0.7 0.6 0.8 ] mean value: 0.6864285714285714 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.76363636 0.80909091 0.38181818 0.75909091 0.75 0.7 0.6 0.7 0.6 0.8 ] mean value: 0.6863636363636363 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.61538462 0.66666667 0.23529412 0.64285714 0.64285714 0.5 0.42857143 0.53846154 0.42857143 0.66666667] mean value: 0.5365330747683689 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.18 MCC on Training: 0.38 Running classifier: 6 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.34397268 0.34610868 0.34610319 0.33869362 0.33537221 0.34207964 0.33910632 0.33830881 0.34199142 0.33675289] mean value: 0.3408489465713501 key: score_time value: [0.00911736 0.0091939 0.00914049 0.00893879 0.00896072 0.00891495 0.01012731 0.008883 0.00888801 0.0089674 ] mean value: 0.009113192558288574 key: test_mcc value: [ 0.24771685 0.62641448 -0.03739788 0.42727273 0.43643578 0.52414242 0.40824829 0.50251891 0.50251891 0.10050378] mean value: 0.3738374264213426 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.63636364 0.77777778 0.42105263 0.72727273 0.75 0.70588235 0.66666667 0.73684211 0.76190476 0.57142857] mean value: 0.6755191231197423 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.58333333 0.875 0.5 0.72727273 0.64285714 0.85714286 0.75 0.77777778 0.72727273 0.54545455] mean value: 0.6986111111111111 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.7 0.7 0.36363636 0.72727273 0.9 0.6 0.6 0.7 0.8 0.6 ] mean value: 0.6690909090909091 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.61904762 0.80952381 0.47619048 0.71428571 0.7 0.75 0.7 0.75 0.75 0.55 ] mean value: 0.6819047619047619 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.62272727 0.80454545 0.48181818 0.71363636 0.7 0.75 0.7 0.75 0.75 0.55 ] mean value: 0.6822727272727274 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.46666667 0.63636364 0.26666667 0.57142857 0.6 0.54545455 0.5 0.58333333 0.61538462 0.4 ] mean value: 0.5185298035298036 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.22 MCC on Training: 0.37 Running classifier: 7 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.00894833 0.00860548 0.00888491 0.00909901 0.00859594 0.00905752 0.00907755 0.00877762 0.00892925 0.00897002] mean value: 0.008894562721252441 key: score_time value: [0.008497 0.00874662 0.00850463 0.00912356 0.00864863 0.00930023 0.0085597 0.00879049 0.00857687 0.00872207] mean value: 0.008746981620788574 key: test_mcc value: [ 0.23636364 0.53935989 -0.15569979 0.13762047 0.6 0.2 0.20412415 0.20412415 0.57735027 0. ] mean value: 0.2543242767796209 key: train_mcc value: [0.50313879 0.473855 0.46989127 0.45654007 0.46618006 0.38881689 0.48566647 0.44151079 0.44343957 0.48271966] mean value: 0.46117585836863784 key: test_fscore value: [0.6 0.70588235 0.14285714 0.60869565 0.8 0.6 0.55555556 0.55555556 0.66666667 0.375 ] mean value: 0.561021292575001 key: train_fscore value: [0.70807453 0.6835443 0.56923077 0.67924528 0.67515924 0.65454545 0.6918239 0.69047619 0.66242038 0.69565217] mean value: 0.6710172226348748 key: test_precision value: [0.6 0.85714286 0.33333333 0.58333333 0.8 0.6 0.625 0.625 1. 0.5 ] mean value: 0.6523809523809524 key: train_precision value: [0.82608696 0.81818182 0.94871795 0.79411765 0.81538462 0.73972603 0.82089552 0.76315789 0.8 0.8115942 ] mean value: 0.8137862633285657 key: test_recall value: [0.6 0.6 0.09090909 0.63636364 0.8 0.6 0.5 0.5 0.5 0.3 ] mean value: 0.5127272727272727 key: train_recall value: [0.61956522 0.58695652 0.40659341 0.59340659 0.57608696 0.58695652 0.59782609 0.63043478 0.56521739 0.60869565] mean value: 0.5771739130434782 key: test_accuracy value: [0.61904762 0.76190476 0.42857143 0.57142857 0.8 0.6 0.6 0.6 0.75 0.5 ] mean value: 0.6230952380952381 key: train_accuracy value: [0.7431694 0.72677596 0.69398907 0.72131148 0.72282609 0.69021739 0.73369565 0.7173913 0.71195652 0.73369565] mean value: 0.7195028510334998 key: test_roc_auc value: [0.61818182 0.75454545 0.44545455 0.56818182 0.8 0.6 0.6 0.6 0.75 0.5 ] mean value: 0.6236363636363637 key: train_roc_auc value: [0.74384854 0.72754419 0.69242714 0.72061634 0.72282609 0.69021739 0.73369565 0.7173913 0.71195652 0.73369565] mean value: 0.7194218824653608 key: test_jcc value: [0.42857143 0.54545455 0.07692308 0.4375 0.66666667 0.42857143 0.38461538 0.38461538 0.5 0.23076923] mean value: 0.4083687146187146 key: train_jcc value: [0.54807692 0.51923077 0.39784946 0.51428571 0.50961538 0.48648649 0.52884615 0.52727273 0.4952381 0.53333333] mean value: 0.5060235049751178 MCC on Blind test: 0.2 MCC on Training: 0.25 Running classifier: 8 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.02073383 0.02508855 0.03310585 0.02567482 0.02391768 0.02421927 0.02520251 0.02360702 0.05172229 0.05121708] mean value: 0.03044888973236084 key: score_time value: [0.01222205 0.01239753 0.01323509 0.01237726 0.01243067 0.01234937 0.01234174 0.01228738 0.02204013 0.02369499] mean value: 0.014537620544433593 key: test_mcc value: [ 0.33028913 0.33028913 -0.24771685 -0.03015113 0.31448545 0.40824829 -0.10050378 0.30151134 0.2 0.31448545] mean value: 0.18209370330134653 key: train_mcc value: [1. 1. 1. 0.98912914 0.98918887 0.98918887 0.98918887 1. 0.98918887 0.98918887] mean value: 0.9935073501257141 key: test_fscore value: [0.63157895 0.63157895 0.43478261 0.35294118 0.69565217 0.66666667 0.42105263 0.63157895 0.6 0.58823529] mean value: 0.5654067393547807 key: train_fscore value: [1. 1. 1. 0.99447514 0.99453552 0.99453552 0.99453552 1. 0.99453552 0.99453552] mean value: 0.9967152733749964 key: test_precision value: [0.66666667 0.66666667 0.41666667 0.5 0.61538462 0.75 0.44444444 0.66666667 0.6 0.71428571] mean value: 0.6040781440781441 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.6 0.6 0.45454545 0.27272727 0.8 0.6 0.4 0.6 0.6 0.5 ] mean value: 0.5427272727272727 key: train_recall value: [1. 1. 1. 0.98901099 0.98913043 0.98913043 0.98913043 1. 0.98913043 0.98913043] mean value: 0.9934663162924033 key: test_accuracy value: [0.66666667 0.66666667 0.38095238 0.47619048 0.65 0.7 0.45 0.65 0.6 0.65 ] mean value: 0.589047619047619 key: train_accuracy value: [1. 1. 1. 0.99453552 0.99456522 0.99456522 0.99456522 1. 0.99456522 0.99456522] mean value: 0.9967361606082206 key: test_roc_auc value: [0.66363636 0.66363636 0.37727273 0.48636364 0.65 0.7 0.45 0.65 0.6 0.65 ] mean value: 0.5890909090909091 key: train_roc_auc value: [1. 1. 1. 0.99450549 0.99456522 0.99456522 0.99456522 1. 0.99456522 0.99456522] mean value: 0.9967331581462016 key: test_jcc value: [0.46153846 0.46153846 0.27777778 0.21428571 0.53333333 0.5 0.26666667 0.46153846 0.42857143 0.41666667] mean value: 0.4021916971916972 key: train_jcc value: [1. 1. 1. 0.98901099 0.98913043 0.98913043 0.98913043 1. 0.98913043 0.98913043] mean value: 0.9934663162924033 MCC on Blind test: 0.07 MCC on Training: 0.18 Running classifier: 9 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.01371384 0.00964284 0.00867152 0.00956559 0.00886345 0.01003933 0.00953054 0.01029038 0.00983572 0.01019597] mean value: 0.010034918785095215 key: score_time value: [0.01153445 0.0099287 0.01007056 0.01052189 0.01044273 0.01050925 0.0109911 0.01085281 0.010782 0.01078105] mean value: 0.01064145565032959 key: test_mcc value: [ 0.33028913 0.04545455 -0.24771685 0.05504819 0.10050378 0.43643578 0.21821789 0.10050378 -0.20412415 0.30151134] mean value: 0.11361234492009924 key: train_mcc value: [0.5090891 0.53040988 0.5300442 0.53010033 0.51089975 0.45662965 0.53339702 0.4680831 0.50047326 0.45695385] mean value: 0.5026080145696427 key: test_fscore value: [0.63157895 0.5 0.43478261 0.5 0.57142857 0.625 0.66666667 0.57142857 0.33333333 0.63157895] mean value: 0.5465797646289636 key: train_fscore value: [0.74860335 0.76243094 0.76243094 0.76502732 0.75675676 0.72527473 0.75977654 0.72625698 0.74444444 0.72222222] mean value: 0.7473224221063939 key: test_precision value: [0.66666667 0.5 0.41666667 0.55555556 0.54545455 0.83333333 0.57142857 0.54545455 0.375 0.66666667] mean value: 0.5676226551226551 key: train_precision value: [0.77011494 0.7752809 0.76666667 0.76086957 0.75268817 0.73333333 0.7816092 0.74712644 0.76136364 0.73863636] mean value: 0.7587689210849451 key: test_recall value: [0.6 0.5 0.45454545 0.45454545 0.6 0.5 0.8 0.6 0.3 0.6 ] mean value: 0.5409090909090909 key: train_recall value: [0.72826087 0.75 0.75824176 0.76923077 0.76086957 0.7173913 0.73913043 0.70652174 0.72826087 0.70652174] mean value: 0.7364429049211658 key: test_accuracy value: [0.66666667 0.52380952 0.38095238 0.52380952 0.55 0.7 0.6 0.55 0.4 0.65 ] mean value: 0.5545238095238096 key: train_accuracy value: [0.75409836 0.76502732 0.76502732 0.76502732 0.75543478 0.72826087 0.76630435 0.73369565 0.75 0.72826087] mean value: 0.7511136849607983 key: test_roc_auc value: [0.66363636 0.52272727 0.37727273 0.52727273 0.55 0.7 0.6 0.55 0.4 0.65 ] mean value: 0.5540909090909091 key: train_roc_auc value: [0.75424032 0.76510989 0.76499044 0.76505017 0.75543478 0.72826087 0.76630435 0.73369565 0.75 0.72826087] mean value: 0.7511347348303871 key: test_jcc value: [0.46153846 0.33333333 0.27777778 0.33333333 0.4 0.45454545 0.5 0.4 0.2 0.46153846] mean value: 0.3822066822066822 key: train_jcc value: [0.59821429 0.61607143 0.61607143 0.61946903 0.60869565 0.56896552 0.61261261 0.57017544 0.59292035 0.56521739] mean value: 0.596841313531686 MCC on Blind test: 0.11 MCC on Training: 0.11 Running classifier: 10 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.03091574 0.06987 0.0774343 0.04010224 0.05441117 0.05425358 0.05427718 0.05345273 0.05521059 0.05271173] mean value: 0.05426392555236816 key: score_time value: [0.0241518 0.03670621 0.03421402 0.02343988 0.02423882 0.01830554 0.02095246 0.02100205 0.02095461 0.02170658] mean value: 0.024567198753356934 key: test_mcc value: [ 0.4719399 0.44038551 -0.05504819 0.24771685 0.40824829 0.40824829 0.40824829 0.10482848 0.10050378 0.50251891] mean value: 0.30375901128912347 key: train_mcc value: [0.8690718 0.87983755 0.86904056 0.87982321 0.8804868 0.90222721 0.85874638 0.90222721 0.9132593 0.93478261] mean value: 0.8889502640167564 key: test_fscore value: [0.75 0.72727273 0.52173913 0.6 0.66666667 0.66666667 0.72727273 0.47058824 0.57142857 0.73684211] mean value: 0.6438476830299417 key: train_fscore value: [0.93406593 0.93989071 0.93333333 0.93922652 0.93989071 0.95081967 0.92972973 0.95081967 0.95604396 0.9673913 ] mean value: 0.9441211541885117 key: test_precision value: [0.64285714 0.66666667 0.5 0.66666667 0.75 0.75 0.66666667 0.57142857 0.54545455 0.77777778] mean value: 0.6537518037518038 key: train_precision value: [0.94444444 0.94505495 0.94382022 0.94444444 0.94505495 0.95604396 0.92473118 0.95604396 0.96666667 0.9673913 ] mean value: 0.9493696069615984 key: test_recall value: [0.9 0.8 0.54545455 0.54545455 0.6 0.6 0.8 0.4 0.6 0.7 ] mean value: 0.6490909090909092 key: train_recall value: [0.92391304 0.93478261 0.92307692 0.93406593 0.93478261 0.94565217 0.93478261 0.94565217 0.94565217 0.9673913 ] mean value: 0.9389751552795031 key: test_accuracy value: [0.71428571 0.71428571 0.47619048 0.61904762 0.7 0.7 0.7 0.55 0.55 0.75 ] mean value: 0.6473809523809524 key: train_accuracy value: [0.93442623 0.93989071 0.93442623 0.93989071 0.94021739 0.95108696 0.92934783 0.95108696 0.95652174 0.9673913 ] mean value: 0.9444286053694466 key: test_roc_auc value: [0.72272727 0.71818182 0.47272727 0.62272727 0.7 0.7 0.7 0.55 0.55 0.75 ] mean value: 0.6486363636363637 key: train_roc_auc value: [0.93448399 0.93991878 0.93436455 0.93985905 0.94021739 0.95108696 0.92934783 0.95108696 0.95652174 0.9673913 ] mean value: 0.9444278547539418 key: test_jcc value: [0.6 0.57142857 0.35294118 0.42857143 0.5 0.5 0.57142857 0.30769231 0.4 0.58333333] mean value: 0.48153953889248 key: train_jcc value: [0.87628866 0.88659794 0.875 0.88541667 0.88659794 0.90625 0.86868687 0.90625 0.91578947 0.93684211] mean value: 0.8943719650383379 MCC on Blind test: 0.07 MCC on Training: 0.3 Running classifier: 11 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.03512788 0.03591132 0.03635764 0.03171086 0.03517294 0.03238392 0.0362072 0.03844643 0.03474855 0.03140354] mean value: 0.03474702835083008 key: score_time value: [0.01211286 0.01194787 0.01189041 0.01224065 0.01199293 0.01208615 0.01194978 0.01193953 0.01187134 0.01200676] mean value: 0.012003827095031738 key: test_mcc value: [ 0.39196475 0.62641448 -0.23636364 0.23636364 0.61237244 0.52414242 0.6 0.30151134 0.40824829 0.40824829] mean value: 0.38729020108190026 key: train_mcc value: [0.6941823 0.71597567 0.72682752 0.67212136 0.64134224 0.65217391 0.66308265 0.66339628 0.70656348 0.68482306] mean value: 0.6820488467461491 key: test_fscore value: [0.72 0.77777778 0.38095238 0.63636364 0.81818182 0.70588235 0.8 0.63157895 0.72727273 0.66666667] mean value: 0.6864676307524606 key: train_fscore value: [0.84615385 0.86021505 0.86338798 0.83516484 0.82162162 0.82608696 0.83243243 0.82872928 0.85405405 0.84324324] mean value: 0.8411089302865244 key: test_precision value: [0.6 0.875 0.4 0.63636364 0.75 0.85714286 0.8 0.66666667 0.66666667 0.75 ] mean value: 0.7001839826839829 key: train_precision value: [0.85555556 0.85106383 0.85869565 0.83516484 0.8172043 0.82608696 0.82795699 0.84269663 0.84946237 0.83870968] mean value: 0.8402596791750094 key: test_recall value: [0.9 0.7 0.36363636 0.63636364 0.9 0.6 0.8 0.6 0.8 0.6 ] mean value: 0.69 key: train_recall value: [0.83695652 0.86956522 0.86813187 0.83516484 0.82608696 0.82608696 0.83695652 0.81521739 0.85869565 0.84782609] mean value: 0.842068800764453 key: test_accuracy value: [0.66666667 0.80952381 0.38095238 0.61904762 0.8 0.75 0.8 0.65 0.7 0.7 ] mean value: 0.6876190476190477 key: train_accuracy value: [0.84699454 0.8579235 0.86338798 0.83606557 0.82065217 0.82608696 0.83152174 0.83152174 0.85326087 0.8423913 ] mean value: 0.840980636730815 key: test_roc_auc value: [0.67727273 0.80454545 0.38181818 0.61818182 0.8 0.75 0.8 0.65 0.7 0.7 ] mean value: 0.6881818181818182 key: train_roc_auc value: [0.84704969 0.85785953 0.86341376 0.83606068 0.82065217 0.82608696 0.83152174 0.83152174 0.85326087 0.8423913 ] mean value: 0.8409818442427138 key: test_jcc value: [0.5625 0.63636364 0.23529412 0.46666667 0.69230769 0.54545455 0.66666667 0.46153846 0.57142857 0.5 ] mean value: 0.5338220358073299 key: train_jcc value: [0.73333333 0.75471698 0.75961538 0.71698113 0.69724771 0.7037037 0.71296296 0.70754717 0.74528302 0.72897196] mean value: 0.7260363355541017 MCC on Blind test: 0.28 MCC on Training: 0.39 Running classifier: 12 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(cv=3, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(cv=3, random_state=42))]) key: fit_time value: [0.56764984 0.64914012 0.58135104 0.43164515 0.64552259 0.45962858 0.53401828 0.45947456 0.57434297 0.52969575] mean value: 0.5432468891143799 key: score_time value: [0.01201439 0.01192808 0.01198888 0.01936817 0.01248193 0.0124948 0.01249003 0.01257896 0.01270342 0.01291656] mean value: 0.01309652328491211 key: test_mcc value: [ 0.24771685 0.71562645 -0.23636364 0.52295779 0.52414242 0.40824829 0.31448545 0.6 0.20412415 0.40824829] mean value: 0.37091860420941425 key: train_mcc value: [0.67284263 0.73784208 0.63939322 0.6066436 0.93500357 0.75004431 0.74070734 0.61960182 0.76086957 0.98918887] mean value: 0.7452137013762081 key: test_fscore value: [0.63636364 0.84210526 0.38095238 0.7826087 0.70588235 0.66666667 0.58823529 0.8 0.63636364 0.66666667] mean value: 0.670584459288188 key: train_fscore value: [0.83333333 0.87096774 0.81967213 0.8 0.96703297 0.87431694 0.86516854 0.81081081 0.88043478 0.99453552] mean value: 0.8716272765211068 key: test_precision value: [0.58333333 0.88888889 0.4 0.75 0.85714286 0.75 0.71428571 0.8 0.58333333 0.75 ] mean value: 0.7076984126984127 key: train_precision value: [0.85227273 0.86170213 0.81521739 0.80898876 0.97777778 0.87912088 0.89534884 0.80645161 0.88043478 1. ] mean value: 0.8777314899901473 key: test_recall value: [0.7 0.8 0.36363636 0.81818182 0.6 0.6 0.5 0.8 0.7 0.6 ] mean value: 0.6481818181818182 key: train_recall value: [0.81521739 0.88043478 0.82417582 0.79120879 0.95652174 0.86956522 0.83695652 0.81521739 0.88043478 0.98913043] mean value: 0.8658862876254181 key: test_accuracy value: [0.61904762 0.85714286 0.38095238 0.76190476 0.75 0.7 0.65 0.8 0.6 0.7 ] mean value: 0.6819047619047619 key: train_accuracy value: [0.83606557 0.86885246 0.81967213 0.80327869 0.9673913 0.875 0.86956522 0.80978261 0.88043478 0.99456522] mean value: 0.87246079828938 key: test_roc_auc value: [0.62272727 0.85454545 0.38181818 0.75909091 0.75 0.7 0.65 0.8 0.6 0.7 ] mean value: 0.6818181818181819 key: train_roc_auc value: [0.83618012 0.86878882 0.81969661 0.80321309 0.9673913 0.875 0.86956522 0.80978261 0.88043478 0.99456522] mean value: 0.8724617773530816 key: test_jcc value: [0.46666667 0.72727273 0.23529412 0.64285714 0.54545455 0.5 0.41666667 0.66666667 0.46666667 0.5 ] mean value: 0.5167545199898141 key: train_jcc value: [0.71428571 0.77142857 0.69444444 0.66666667 0.93617021 0.77669903 0.76237624 0.68181818 0.78640777 0.98913043] mean value: 0.7779427259932412 MCC on Blind test: 0.26 MCC on Training: 0.37 Running classifier: 13 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [0.86243176 0.77574396 0.76819253 0.88896656 0.83166409 0.77718592 0.95756841 0.76066542 0.89791846 0.76680827] mean value: 0.8287145376205445 key: score_time value: [0.01264477 0.01282716 0.01255846 0.01267576 0.01266408 0.01263833 0.01291108 0.01260185 0.01307535 0.01271558] mean value: 0.012731242179870605 key: test_mcc value: [ 0.4719399 0.61818182 -0.23636364 0.24771685 0.2 0.40824829 0.30151134 0.2 0.10482848 0.50251891] mean value: 0.2818581959039307 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.75 0.8 0.38095238 0.6 0.6 0.66666667 0.63157895 0.6 0.60869565 0.73684211] mean value: 0.6374735752424538 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.64285714 0.8 0.4 0.66666667 0.6 0.75 0.66666667 0.6 0.53846154 0.77777778] mean value: 0.6442429792429792 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.9 0.8 0.36363636 0.54545455 0.6 0.6 0.6 0.6 0.7 0.7 ] mean value: 0.640909090909091 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.71428571 0.80952381 0.38095238 0.61904762 0.6 0.7 0.65 0.6 0.55 0.75 ] mean value: 0.6373809523809524 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.72272727 0.80909091 0.38181818 0.62272727 0.6 0.7 0.65 0.6 0.55 0.75 ] mean value: 0.6386363636363636 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.6 0.66666667 0.23529412 0.42857143 0.42857143 0.5 0.46153846 0.42857143 0.4375 0.58333333] mean value: 0.47700468648998057 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.07 MCC on Training: 0.28 Running classifier: 14 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.01240444 0.01228285 0.00906777 0.00894213 0.00891995 0.00952387 0.00879097 0.00856948 0.00890493 0.00886798] mean value: 0.009627437591552735 key: score_time value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) [0.01195168 0.01095486 0.00903559 0.00856376 0.00856471 0.00926995 0.00844121 0.00846004 0.00851154 0.00846577] mean value: 0.009221911430358887 key: test_mcc value: [ 0.24771685 0.33709993 -0.33028913 0.23373675 0.31448545 0. 0.2 0.21821789 0.50251891 0.40824829] mean value: 0.21317349356952953 key: train_mcc value: [0.3992114 0.35589597 0.46461881 0.40051023 0.40219767 0.35973847 0.44631179 0.35888651 0.47837392 0.43488538] mean value: 0.410063016614583 key: test_fscore value: [0.63636364 0.58823529 0.3 0.66666667 0.69565217 0.5 0.6 0.5 0.76190476 0.66666667] mean value: 0.5915489199632422 key: train_fscore value: [0.6961326 0.67039106 0.72625698 0.68208092 0.69945355 0.66666667 0.7150838 0.67403315 0.74193548 0.71428571] mean value: 0.6986319931023182 key: test_precision value: [0.58333333 0.71428571 0.33333333 0.61538462 0.61538462 0.5 0.6 0.66666667 0.72727273 0.75 ] mean value: 0.6105661005661006 key: train_precision value: [0.70786517 0.68965517 0.73863636 0.7195122 0.7032967 0.69411765 0.73563218 0.68539326 0.73404255 0.72222222] mean value: 0.7130373467815685 key: test_recall value: [0.7 0.5 0.27272727 0.72727273 0.8 0.5 0.6 0.4 0.8 0.6 ] mean value: 0.59 key: train_recall value: [0.68478261 0.65217391 0.71428571 0.64835165 0.69565217 0.64130435 0.69565217 0.66304348 0.75 0.70652174] mean value: 0.6851767797419972 key: test_accuracy value: [0.61904762 0.66666667 0.33333333 0.61904762 0.65 0.5 0.6 0.6 0.75 0.7 ] mean value: 0.6038095238095238 key: train_accuracy value: [0.69945355 0.67759563 0.73224044 0.69945355 0.70108696 0.67934783 0.72282609 0.67934783 0.73913043 0.7173913 ] mean value: 0.7047873604181516 key: test_roc_auc value: [0.62272727 0.65909091 0.33636364 0.61363636 0.65 0.5 0.6 0.6 0.75 0.7 ] mean value: 0.6031818181818182 key: train_roc_auc value: [0.69953416 0.67773531 0.73214286 0.69917582 0.70108696 0.67934783 0.72282609 0.67934783 0.73913043 0.7173913 ] mean value: 0.7047718585762064 key: test_jcc value: [0.46666667 0.41666667 0.17647059 0.5 0.53333333 0.33333333 0.42857143 0.33333333 0.61538462 0.5 ] mean value: 0.4303759965524671 key: train_jcc value: [0.53389831 0.50420168 0.57017544 0.51754386 0.53781513 0.5 0.55652174 0.50833333 0.58974359 0.55555556] mean value: 0.5373788627815961 MCC on Blind test: 0.17 MCC on Training: 0.21 Running classifier: 15 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.00987649 0.0105772 0.00893092 0.01026297 0.00944495 0.01058507 0.01002407 0.01066685 0.01109266 0.01038384] mean value: 0.010184502601623536 key: score_time value: [0.00861835 0.00959992 0.00985694 0.01016545 0.00955486 0.01020241 0.00971508 0.01007748 0.00920224 0.00977921] mean value: 0.00967719554901123 key: test_mcc value: [-0.13762047 0.03739788 -0.13483997 -0.13762047 0. -0.40824829 -0.30151134 -0.10050378 0.61237244 0.10050378] mean value: -0.047007023351973244 key: train_mcc value: [0.44274456 0.33401124 0.44446428 0.42461204 0.45016908 0.38226832 0.45016908 0.3299294 0.45169638 0.3815408 ] mean value: 0.4091605173950096 key: test_fscore value: [0.45454545 0.44444444 0.33333333 0.4 0.44444444 0.36363636 0.38095238 0.42105263 0.81818182 0.52631579] mean value: 0.4586906660590871 key: train_fscore value: [0.72727273 0.65921788 0.70520231 0.68639053 0.70175439 0.70466321 0.70175439 0.63529412 0.69822485 0.6779661 ] mean value: 0.6897740504828843 key: test_precision value: [0.41666667 0.5 0.42857143 0.44444444 0.5 0.33333333 0.36363636 0.44444444 0.75 0.55555556] mean value: 0.4736652236652237 key: train_precision value: [0.71578947 0.67816092 0.74390244 0.74358974 0.75949367 0.67326733 0.75949367 0.69230769 0.76623377 0.70588235] mean value: 0.7238121055826034 key: test_recall value: [0.5 0.4 0.27272727 0.36363636 0.4 0.4 0.4 0.4 0.9 0.5 ] mean value: 0.4536363636363637 key: train_recall value: [0.73913043 0.64130435 0.67032967 0.63736264 0.65217391 0.73913043 0.65217391 0.58695652 0.64130435 0.65217391] mean value: 0.6612040133779264 key: test_accuracy value: [0.42857143 0.52380952 0.42857143 0.42857143 0.5 0.3 0.35 0.45 0.8 0.55 ] mean value: 0.47595238095238096 key: train_accuracy value: [0.72131148 0.66666667 0.72131148 0.71038251 0.72282609 0.69021739 0.72282609 0.66304348 0.72282609 0.69021739] mean value: 0.703162865288667 key: test_roc_auc value: [0.43181818 0.51818182 0.43636364 0.43181818 0.5 0.3 0.35 0.45 0.8 0.55 ] mean value: 0.47681818181818175 key: train_roc_auc value: [0.72121357 0.66680602 0.7210344 0.70998567 0.72282609 0.69021739 0.72282609 0.66304348 0.72282609 0.69021739] mean value: 0.7030996177735307 key: test_jcc value: [0.29411765 0.28571429 0.2 0.25 0.28571429 0.22222222 0.23529412 0.26666667 0.69230769 0.35714286] mean value: 0.30891797744738925 key: train_jcc value: [0.57142857 0.49166667 0.54464286 0.52252252 0.54054054 0.544 0.54054054 0.46551724 0.53636364 0.51282051] mean value: 0.5270043089405159 MCC on Blind test: 0.19 MCC on Training: -0.05 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.01122332 0.01500082 0.01402903 0.01458526 0.01409435 0.01384735 0.01384187 0.01446033 0.01395488 0.01537752] mean value: 0.014041471481323241 key: score_time value: [0.00947785 0.01155901 0.01151299 0.01164794 0.01194215 0.0115335 0.01160908 0.01164699 0.01181078 0.01171017] mean value: 0.011445045471191406 key: test_mcc value: [ 0.14545455 0.53935989 -0.15894099 0.34027852 0. 0.31448545 0.61237244 0.4 -0.22941573 0.33333333] mean value: 0.22969274569882034 key: train_mcc value: [0.66822343 0.67869468 0.63619347 0.42807078 0.21320072 0.41082092 0.61586822 0.57906602 0.34921515 0.34921515] mean value: 0.4928568533706791 key: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") test_fscore value: [0.57142857 0.70588235 0.5 0.73333333 0. 0.69565217 0.77777778 0.7 0. 0.33333333] mean value: 0.5017407542727235 key: train_fscore value: [0.81871345 0.79746835 0.82653061 0.74476987 0.16 0.74074074 0.78823529 0.75609756 0.35714286 0.35714286] mean value: 0.6346841601564375 key: test_precision value: [0.54545455 0.85714286 0.46153846 0.57894737 0. 0.61538462 0.875 0.7 0. 1. ] mean value: 0.5633467847941531 key: train_precision value: [0.88607595 0.95454545 0.77142857 0.60135135 1. 0.59602649 0.85897436 0.86111111 1. 1. ] mean value: 0.852951328684416 key: test_recall value: [0.6 0.6 0.54545455 1. 0. 0.8 0.7 0.7 0. 0.2 ] mean value: 0.5145454545454545 key: train_recall value: [0.76086957 0.68478261 0.89010989 0.97802198 0.08695652 0.97826087 0.72826087 0.67391304 0.2173913 0.2173913 ] mean value: 0.621595795508839 key: test_accuracy value: [0.57142857 0.76190476 0.42857143 0.61904762 0.5 0.65 0.8 0.7 0.45 0.6 ] mean value: 0.608095238095238 key: train_accuracy value: [0.83060109 0.82513661 0.81420765 0.66666667 0.54347826 0.6576087 0.80434783 0.7826087 0.60869565 0.60869565] mean value: 0.714204680446662 key: test_roc_auc value: [0.57272727 0.75454545 0.42272727 0.6 0.5 0.65 0.8 0.7 0.45 0.6 ] mean value: 0.605 key: train_roc_auc value: [0.83098423 0.82590779 0.81462016 0.66835882 0.54347826 0.6576087 0.80434783 0.7826087 0.60869565 0.60869565] mean value: 0.7145305781175347 key: test_jcc value: [0.4 0.54545455 0.33333333 0.57894737 0. 0.53333333 0.63636364 0.53846154 0. 0.2 ] mean value: 0.376589375536744 key: train_jcc value: [0.69306931 0.66315789 0.70434783 0.59333333 0.08695652 0.58823529 0.65048544 0.60784314 0.2173913 0.2173913 ] mean value: 0.502221135978836 MCC on Blind test: -0.07 MCC on Training: 0.23 Running classifier: 17 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.01840901 0.02004766 0.02134705 0.02158117 0.02141857 0.02263546 0.03421783 0.0221734 0.05485439 0.0573411 ] mean value: 0.029402565956115723 key: score_time value: [0.00996065 0.01228166 0.0123682 0.01242399 0.01232433 0.01233125 0.01704669 0.01650405 0.01985931 0.01245689] mean value: 0.013755702972412109 key: test_mcc value: [ 0.44038551 0.13483997 -0.06741999 -0.03015113 0.31448545 0.52414242 -0.20412415 -0.10050378 0.43643578 0.50251891] mean value: 0.19506089885603645 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.72727273 0.47058824 0.56 0.35294118 0.69565217 0.70588235 0.33333333 0.47619048 0.625 0.73684211] mean value: 0.568370258067862 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.66666667 0.57142857 0.5 0.5 0.61538462 0.85714286 0.375 0.45454545 0.83333333 0.77777778] mean value: 0.6151279276279276 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.8 0.4 0.63636364 0.27272727 0.8 0.6 0.3 0.5 0.5 0.7 ] mean value: 0.550909090909091 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.71428571 0.57142857 0.47619048 0.47619048 0.65 0.75 0.4 0.45 0.7 0.75 ] mean value: 0.5938095238095238 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.71818182 0.56363636 0.46818182 0.48636364 0.65 0.75 0.4 0.45 0.7 0.75 ] mean value: 0.5936363636363636 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.57142857 0.30769231 0.38888889 0.21428571 0.53333333 0.54545455 0.2 0.3125 0.45454545 0.58333333] mean value: 0.4111462148962149 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.15 MCC on Training: 0.2 Running classifier: 18 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42))]) key: fit_time value: [0.60655689 0.62713599 0.57633662 0.65300512 0.63526559 0.58594322 0.63138986 0.64699006 0.60749125 0.62915516] mean value: 0.6199269771575928 key: score_time value: [0.18788815 0.15336347 0.15859509 0.14706421 0.1436429 0.15217519 0.13325787 0.15179062 0.16755629 0.16493154] mean value: 0.1560265302658081 key: test_mcc value: [ 0.35527986 0.71562645 -0.04545455 0.71562645 0.43643578 0.40824829 0.31448545 0.50251891 0.30151134 0.50251891] mean value: 0.42067968872023365 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.69565217 0.84210526 0.47619048 0.86956522 0.75 0.66666667 0.58823529 0.76190476 0.66666667 0.76190476] mean value: 0.7078891281913223 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.61538462 0.88888889 0.5 0.83333333 0.64285714 0.75 0.71428571 0.72727273 0.63636364 0.72727273] mean value: 0.7035658785658787 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.8 0.8 0.45454545 0.90909091 0.9 0.6 0.5 0.8 0.7 0.8 ] mean value: 0.7263636363636363 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.66666667 0.85714286 0.47619048 0.85714286 0.7 0.7 0.65 0.75 0.65 0.75 ] mean value: 0.7057142857142857 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.67272727 0.85454545 0.47727273 0.85454545 0.7 0.7 0.65 0.75 0.65 0.75 ] mean value: 0.705909090909091 key: train_roc_auc value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.53333333 0.72727273 0.3125 0.76923077 0.6 0.5 0.41666667 0.61538462 0.5 0.61538462] mean value: 0.5589772727272727 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.19 MCC on Training: 0.42 Running classifier: 19 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea...age_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42))]) key: fit_time value: [0.92950201 0.90295649 0.9042275 0.91817737 0.92712641 0.92445517 0.93148565 0.90554714 0.97470713 0.91071177] mean value: 0.9228896617889404 key: score_time value: [0.22337008 0.21807456 0.2612679 0.23808908 0.21309185 0.18151736 0.22212005 0.123209 0.20945477 0.21260834] mean value: 0.21028029918670654 key: test_mcc value: [ 0.35527986 0.62641448 -0.03739788 0.71562645 0.52414242 0.52414242 0.43643578 0.8 0.50251891 0.61237244] mean value: 0.505953486775998 key: train_mcc value: [0.91279418 0.90301428 0.86964903 0.86964903 0.90222721 0.88090325 0.90222721 0.90222721 0.88090325 0.90265395] mean value: 0.8926248607419005 key: test_fscore value: [0.69565217 0.77777778 0.42105263 0.86956522 0.7826087 0.70588235 0.625 0.9 0.76190476 0.77777778] mean value: 0.7317221388936963 key: train_fscore value: [0.95604396 0.94972067 0.93258427 0.93258427 0.95081967 0.93922652 0.95081967 0.95081967 0.93922652 0.95027624] mean value: 0.9452121463922258 key: test_precision value: [0.61538462 0.875 0.5 0.83333333 0.69230769 0.85714286 0.83333333 0.9 0.72727273 0.875 ] mean value: 0.7708774558774559 key: train_precision value: [0.96666667 0.97701149 0.95402299 0.95402299 0.95604396 0.95505618 0.95604396 0.95604396 0.95505618 0.96629213] mean value: 0.9596260500444925 key: test_recall value: [0.8 0.7 0.36363636 0.90909091 0.9 0.6 0.5 0.9 0.8 0.7 ] mean value: 0.7172727272727272 key: train_recall value: [0.94565217 0.92391304 0.91208791 0.91208791 0.94565217 0.92391304 0.94565217 0.94565217 0.92391304 0.93478261] mean value: 0.9313306258958434 key: test_accuracy value: [0.66666667 0.80952381 0.47619048 0.85714286 0.75 0.75 0.7 0.9 0.75 0.8 ] mean value: 0.7459523809523809 key: train_accuracy value: [0.95628415 0.95081967 0.93442623 0.93442623 0.95108696 0.94021739 0.95108696 0.95108696 0.94021739 0.95108696] mean value: 0.9460738892848657 key: test_roc_auc value: [0.67272727 0.80454545 0.48181818 0.85454545 0.75 0.75 0.7 0.9 0.75 0.8 ] mean value: 0.7463636363636363 key: train_roc_auc value: [0.95634257 0.95096751 0.93430483 0.93430483 0.95108696 0.94021739 0.95108696 0.95108696 0.94021739 0.95108696] mean value: 0.9460702341137124 key: test_jcc value: [0.53333333 0.63636364 0.26666667 0.76923077 0.64285714 0.54545455 0.45454545 0.81818182 0.61538462 0.63636364] mean value: 0.5918381618381618 key: train_jcc value: [0.91578947 0.90425532 0.87368421 0.87368421 0.90625 0.88541667 0.90625 0.90625 0.88541667 0.90526316] mean value: 0.8962259705113848 MCC on Blind test: 0.22 MCC on Training: 0.51 Running classifier: 20 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.03250384 0.03331327 0.0345366 0.04753995 0.01446009 0.0135591 0.03353667 0.03368068 0.03377533 0.03360653] mean value: 0.031051206588745116 key: score_time value: [0.0211575 0.02085042 0.02350044 0.02834034 0.01169777 0.01160455 0.02165532 0.01738667 0.02383614 0.02038193] mean value: 0.02004110813140869 key: test_mcc value: [ 0.4719399 0.71562645 -0.14545455 0.23636364 0.4 0.40824829 0.10482848 0.50251891 0.20412415 0.52414242] mean value: 0.34223376873240463 key: train_mcc value: [0.78162705 0.78141424 0.79273269 0.78141424 0.76104942 0.77214976 0.75111009 0.73930515 0.81565116 0.79390046] mean value: 0.7770354262956143 key: test_fscore value: [0.75 0.84210526 0.45454545 0.63636364 0.7 0.66666667 0.47058824 0.76190476 0.63636364 0.70588235] mean value: 0.6624420007237345 key: train_fscore value: [0.89010989 0.89130435 0.89385475 0.89010989 0.88172043 0.8839779 0.87150838 0.86813187 0.90607735 0.89839572] mean value: 0.88751905253208 key: test_precision value: [0.64285714 0.88888889 0.45454545 0.63636364 0.7 0.75 0.57142857 0.72727273 0.58333333 0.85714286] mean value: 0.6811832611832611 key: train_precision value: [0.9 0.89130435 0.90909091 0.89010989 0.87234043 0.8988764 0.89655172 0.87777778 0.92134831 0.88421053] mean value: 0.8941610319891422 key: test_recall value: [0.9 0.8 0.45454545 0.63636364 0.7 0.6 0.4 0.8 0.7 0.6 ] mean value: 0.6590909090909091 key: train_recall value: [0.88043478 0.89130435 0.87912088 0.89010989 0.89130435 0.86956522 0.84782609 0.85869565 0.89130435 0.91304348] mean value: 0.8812709030100334 key: test_accuracy value: [0.71428571 0.85714286 0.42857143 0.61904762 0.7 0.7 0.55 0.75 0.6 0.75 ] mean value: 0.6669047619047619 key: train_accuracy value: [0.89071038 0.89071038 0.89617486 0.89071038 0.88043478 0.88586957 0.875 0.86956522 0.9076087 0.89673913] mean value: 0.888352340223331 key: test_roc_auc value: [0.72272727 0.85454545 0.42727273 0.61818182 0.7 0.7 0.55 0.75 0.6 0.75 ] mean value: 0.6672727272727272 key: train_roc_auc value: [0.89076684 0.89070712 0.89608218 0.89070712 0.88043478 0.88586957 0.875 0.86956522 0.9076087 0.89673913] mean value: 0.8883480649784998 key: test_jcc value: [0.6 0.72727273 0.29411765 0.46666667 0.53846154 0.5 0.30769231 0.61538462 0.46666667 0.54545455] mean value: 0.5061716714657891 key: train_jcc value: [0.8019802 0.80392157 0.80808081 0.8019802 0.78846154 0.79207921 0.77227723 0.76699029 0.82828283 0.81553398] mean value: 0.7979587846980454 MCC on Blind test: 0.21 MCC on Training: 0.34 Running classifier: 21 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=3))]) key: fit_time value: [0.11063552 0.10659289 0.10001874 0.09297252 0.09927845 0.09668517 0.12527776 0.08548212 0.09681463 0.118505 ] mean value: 0.10322628021240235 key: score_time value: [0.02281046 0.02286983 0.02189374 0.02288485 0.01485395 0.02173829 0.02327776 0.0210855 0.02344108 0.02077341] mean value: 0.021562886238098145 key: test_mcc value: [ 0.35527986 0.71562645 -0.23636364 0.23636364 0.61237244 0.40824829 0.70352647 0.50251891 0.50251891 0.40824829] mean value: 0.4208339606099528 key: train_mcc value: [0.67284263 0.78141424 0.67232111 0.65037643 0.61960182 0.77214976 0.63043478 0.61960182 0.68514697 0.6535653 ] mean value: 0.6757454874835254 key: test_fscore value: [0.69565217 0.84210526 0.38095238 0.63636364 0.81818182 0.66666667 0.84210526 0.73684211 0.76190476 0.66666667] mean value: 0.7047440736227921 key: train_fscore value: [0.83333333 0.89130435 0.83695652 0.82222222 0.80874317 0.8839779 0.81521739 0.80874317 0.83977901 0.82022472] mean value: 0.8360501780401407 key: test_precision value: [0.61538462 0.88888889 0.4 0.63636364 0.75 0.75 0.88888889 0.77777778 0.72727273 0.75 ] mean value: 0.7184576534576534 key: train_precision value: [0.85227273 0.89130435 0.82795699 0.83146067 0.81318681 0.8988764 0.81521739 0.81318681 0.85393258 0.84883721] mean value: 0.8446231954247775 key: test_recall value: [0.8 0.8 0.36363636 0.63636364 0.9 0.6 0.8 0.7 0.8 0.6 ] mean value: 0.7 key: train_recall value: [0.81521739 0.89130435 0.84615385 0.81318681 0.80434783 0.86956522 0.81521739 0.80434783 0.82608696 0.79347826] mean value: 0.8278905876731963 key: test_accuracy value: [0.66666667 0.85714286 0.38095238 0.61904762 0.8 0.7 0.85 0.75 0.75 0.7 ] mean value: 0.7073809523809523 key: train_accuracy value: [0.83606557 0.89071038 0.83606557 0.82513661 0.80978261 0.88586957 0.81521739 0.80978261 0.8423913 0.82608696] mean value: 0.8377108576859111 key: test_roc_auc value: [0.67272727 0.85454545 0.38181818 0.61818182 0.8 0.7 0.85 0.75 0.75 0.7 ] mean value: 0.7077272727272728 key: train_roc_auc value: [0.83618012 0.89070712 0.8361204 0.82507167 0.80978261 0.88586957 0.81521739 0.80978261 0.8423913 0.82608696] mean value: 0.8377209746774964 key: test_jcc value: [0.53333333 0.72727273 0.23529412 0.46666667 0.69230769 0.5 0.72727273 0.58333333 0.61538462 0.5 ] mean value: 0.5580865213218156 key: train_jcc value: [0.71428571 0.80392157 0.71962617 0.69811321 0.67889908 0.79207921 0.68807339 0.67889908 0.72380952 0.6952381 ] mean value: 0.7192945045286073 MCC on Blind test: 0.21 MCC on Training: 0.42 Running classifier: 22 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.02786064 0.01290536 0.01256657 0.01212668 0.012532 0.01267529 0.01237583 0.01260543 0.01233387 0.01229 ] mean value: 0.014027166366577148 key: score_time value: [0.01118159 0.01077271 0.01006699 0.01032829 0.01091027 0.01013684 0.01041722 0.01003218 0.0107801 0.0098877 ] mean value: 0.010451388359069825 key: test_mcc value: [ 0.42727273 0.43007562 -0.04545455 0.15894099 0.61237244 0.40824829 0.4 0.70352647 0.2 0.50251891] mean value: 0.3797500889994222 key: train_mcc value: [0.72031795 0.62891698 0.74967214 0.63197246 0.66339628 0.69581661 0.68482306 0.65217391 0.66402486 0.6535653 ] mean value: 0.6744679558861271 key: test_fscore value: [0.7 0.66666667 0.47619048 0.52631579 0.81818182 0.66666667 0.7 0.84210526 0.6 0.73684211] mean value: 0.6732968785600363 key: train_fscore value: [0.85057471 0.81914894 0.8700565 0.80232558 0.82872928 0.84615385 0.84153005 0.82608696 0.82681564 0.82022472] mean value: 0.8331646228031955 key: test_precision value: [0.7 0.75 0.5 0.625 0.75 0.75 0.7 0.88888889 0.6 0.77777778] mean value: 0.7041666666666666 key: train_precision value: [0.90243902 0.80208333 0.89534884 0.85185185 0.84269663 0.85555556 0.84615385 0.82608696 0.85057471 0.84883721] mean value: 0.8521627956175359 key: test_recall value: [0.7 0.6 0.45454545 0.45454545 0.9 0.6 0.7 0.8 0.6 0.7 ] mean value: 0.6509090909090909 key: train_recall value: [0.80434783 0.83695652 0.84615385 0.75824176 0.81521739 0.83695652 0.83695652 0.82608696 0.80434783 0.79347826] mean value: 0.8158743430482561 key: test_accuracy value: [0.71428571 0.71428571 0.47619048 0.57142857 0.8 0.7 0.7 0.85 0.6 0.75 ] mean value: 0.6876190476190476 key: train_accuracy value: [0.8579235 0.81420765 0.87431694 0.81420765 0.83152174 0.84782609 0.8423913 0.82608696 0.83152174 0.82608696] mean value: 0.8366090520313613 key: test_roc_auc value: [0.71363636 0.70909091 0.47727273 0.57727273 0.8 0.7 0.7 0.85 0.6 0.75 ] mean value: 0.6877272727272727 key: train_roc_auc value: [0.85821787 0.81408266 0.87416388 0.81390349 0.83152174 0.84782609 0.8423913 0.82608696 0.83152174 0.82608696] mean value: 0.8365802675585284 key: test_jcc value: [0.53846154 0.5 0.3125 0.35714286 0.69230769 0.5 0.53846154 0.72727273 0.42857143 0.58333333] mean value: 0.5178051115551117 key: train_jcc value: [0.74 0.69369369 0.77 0.66990291 0.70754717 0.73333333 0.72641509 0.7037037 0.7047619 0.6952381 ] mean value: 0.7144595907503033 MCC on Blind test: 0.19 MCC on Training: 0.38 Running classifier: 23 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=12, random_state=42) Running model pipeline: /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:463: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_CV['source_data'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:490: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_BT['source_data'] = 'BT' Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.01061726 0.01599503 0.01381636 0.015347 0.01516914 0.0151341 0.01592612 0.01511931 0.01509166 0.01513815] mean value: 0.01473541259765625 key: score_time value: [0.00974989 0.0115943 0.01154113 0.01193643 0.011868 0.01182556 0.01189089 0.01196408 0.01192689 0.01179433] mean value: 0.011609148979187012 key: test_mcc value: [ 0.4719399 0.39196475 -0.18090681 0.23636364 0.50251891 0.31448545 0.5 0.43643578 0.57735027 0.25 ] mean value: 0.35001518896001327 key: train_mcc value: [0.72710992 0.64138326 0.61477324 0.62351307 0.70689767 0.72166713 0.6871942 0.71892183 0.68434172 0.45696905] mean value: 0.65827710905142 key: test_fscore value: [0.75 0.72 0.53846154 0.63636364 0.73684211 0.58823529 0.57142857 0.75 0.8 0.42857143] mean value: 0.651990257420598 key: train_fscore value: [0.86631016 0.83253589 0.81904762 0.79289941 0.85082873 0.85057471 0.81927711 0.86315789 0.85 0.53543307] mean value: 0.808006458888908 key: test_precision value: [0.64285714 0.6 0.46666667 0.63636364 0.77777778 0.71428571 1. 0.64285714 0.66666667 0.75 ] mean value: 0.6897474747474748 key: train_precision value: [0.85263158 0.74358974 0.72268908 0.85897436 0.86516854 0.90243902 0.91891892 0.83673469 0.78703704 0.97142857] mean value: 0.8459611542119887 key: test_recall value: [0.9 0.9 0.63636364 0.63636364 0.7 0.5 0.4 0.9 1. 0.3 ] mean value: 0.6872727272727273 key: train_recall value: [0.88043478 0.94565217 0.94505495 0.73626374 0.83695652 0.80434783 0.73913043 0.89130435 0.92391304 0.36956522] mean value: 0.8072623029144769 key: test_accuracy value: [0.71428571 0.66666667 0.42857143 0.61904762 0.75 0.65 0.7 0.7 0.75 0.6 ] mean value: 0.6578571428571428 key: train_accuracy value: [0.86338798 0.80874317 0.79234973 0.80874317 0.85326087 0.85869565 0.83695652 0.85869565 0.83695652 0.67934783] mean value: 0.8197137087194107 key: test_roc_auc value: [0.72272727 0.67727273 0.41818182 0.61818182 0.75 0.65 0.7 0.7 0.75 0.6 ] mean value: 0.6586363636363636 key: train_roc_auc value: [0.86329431 0.80799092 0.79317965 0.80834926 0.85326087 0.85869565 0.83695652 0.85869565 0.83695652 0.67934783] mean value: 0.8196727185857622 key: test_jcc value: [0.6 0.5625 0.36842105 0.46666667 0.58333333 0.41666667 0.4 0.6 0.66666667 0.27272727] mean value: 0.49369816586921844 key: train_jcc value: [0.76415094 0.71311475 0.69354839 0.65686275 0.74038462 0.74 0.69387755 0.75925926 0.73913043 0.3655914 ] mean value: 0.6865920087985754 MCC on Blind test: 0.25 MCC on Training: 0.35 Running classifier: 24 Model_name: XGBoost Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.10727715 0.06632137 0.07102609 0.07113671 0.06792641 0.06181931 0.06764174 0.06326175 0.2260747 0.06005001] mean value: 0.08625352382659912 key: score_time value: [0.01102734 0.01088309 0.01088095 0.01047349 0.01039004 0.01017618 0.01084232 0.01088762 0.01097679 0.01104975] mean value: 0.010758757591247559 key: test_mcc value: [ 0.35527986 0.62641448 -0.13762047 0.42727273 0.34641016 0.52414242 0.52414242 0.50251891 0.50251891 0.30151134] mean value: 0.39725907542134814 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.69565217 0.77777778 0.4 0.72727273 0.72 0.70588235 0.70588235 0.73684211 0.76190476 0.63157895] mean value: 0.6862793199382242 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.61538462 0.875 0.44444444 0.72727273 0.6 0.85714286 0.85714286 0.77777778 0.72727273 0.66666667] mean value: 0.7148104673104674 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.8 0.7 0.36363636 0.72727273 0.9 0.6 0.6 0.7 0.8 0.6 ] mean value: 0.679090909090909 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.66666667 0.80952381 0.42857143 0.71428571 0.65 0.75 0.75 0.75 0.75 0.65 ] mean value: 0.6919047619047619 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.67272727 0.80454545 0.43181818 0.71363636 0.65 0.75 0.75 0.75 0.75 0.65 ] mean value: 0.6922727272727274 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.53333333 0.63636364 0.25 0.57142857 0.5625 0.54545455 0.54545455 0.58333333 0.61538462 0.46153846] mean value: 0.5304791042291042 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.23 MCC on Training: 0.4 Extracting tts_split_name: 80_20 Total cols in each df: CV df: 8 metaDF: 17 Adding column: Model_name Total cols in bts df: BT_df: 8 First proceeding to rowbind CV and BT dfs: Final output should have: 25 columns Combinig 2 using pd.concat by row ~ rowbind Checking Dims of df to combine: Dim of CV: (24, 8) Dim of BT: (24, 8) 8 [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... 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Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... 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Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.6s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... 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Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.7s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.7s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.7s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.7s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.7s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.7s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.6s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.6s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.7s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished Number of Common columns: 8 These are: ['source_data', 'Precision', 'JCC', 'MCC', 'Recall', 'Accuracy', 'F1', 'ROC_AUC'] Concatenating dfs with different resampling methods [WF]: Split type: 80_20 No. of dfs combining: 2 PASS: 2 dfs successfully combined nrows in combined_df_wf: 48 ncols in combined_df_wf: 8 PASS: proceeding to merge metadata with CV and BT dfs Adding column: Model_name ========================================================= SUCCESS: Ran multiple classifiers ======================================================= ============================================================== Running several classification models (n): 24 List of models: ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('Gaussian NB', GaussianNB()) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('K-Nearest Neighbors', KNeighborsClassifier()) ('LDA', LinearDiscriminantAnalysis()) ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(cv=3, random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Multinomial', MultinomialNB()) ('Naive Bayes', BernoulliNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=12, random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Random Forest', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42)) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=3)) ('SVC', SVC(random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=12, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ================================================================ Running classifier: 1 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.29381704 0.28368402 0.28417468 0.28646326 0.28940344 0.28440595 0.28675938 0.28905201 0.28920937 0.28127003] mean value: 0.2868239164352417 key: score_time value: [0.01620674 0.01625848 0.01593828 0.01770949 0.01608849 0.01729178 0.01694512 0.0177021 0.01612806 0.01592875] mean value: 0.01661972999572754 key: test_mcc value: [0.66388411 0.82196022 0.7492057 0.57355974 0.67743539 0.59085834 0.71422584 0.65857939 0.79310345 0.72586619] mean value: 0.6968678357712781 key: train_mcc value: [0.82355796 0.81565902 0.82528929 0.80245939 0.84334837 0.81008594 0.83107751 0.82429081 0.83063442 0.83007424] mean value: 0.8236476943664982 key: test_fscore value: [0.83969466 0.912 0.87804878 0.78991597 0.84552846 0.80327869 0.864 0.82758621 0.89655172 0.86666667] mean value: 0.8523271144873201 key: train_fscore value: [0.91316527 0.90892019 0.9147571 0.90262172 0.92307692 0.90619137 0.91658857 0.91380908 0.91712707 0.91666667] mean value: 0.9132923960050695 key: test_precision value: [0.75342466 0.85074627 0.83076923 0.7704918 0.8125 0.77777778 0.81818182 0.84210526 0.89655172 0.83870968] mean value: 0.8191258220913659 key: train_precision value: [0.89724771 0.89795918 0.88318584 0.88929889 0.89891697 0.89279113 0.90221402 0.8898917 0.88928571 0.89350181] mean value: 0.8934292957073987 key: test_recall value: [0.94827586 0.98275862 0.93103448 0.81034483 0.88135593 0.83050847 0.91525424 0.81355932 0.89655172 0.89655172] mean value: 0.8906195207481004 key: train_recall value: [0.92965779 0.92015209 0.9486692 0.91634981 0.94857143 0.92 0.93142857 0.93904762 0.94676806 0.94106464] mean value: 0.9341709216005795 key: test_accuracy value: [0.82051282 0.90598291 0.87179487 0.78632479 0.83760684 0.79487179 0.85470085 0.82905983 0.89655172 0.86206897] mean value: 0.8459475390509873 key: train_accuracy value: [0.91151284 0.90770695 0.91151284 0.90104662 0.92102759 0.90485252 0.91531874 0.91151284 0.91444867 0.91444867] mean value: 0.9113388299392575 key: test_roc_auc value: [0.82159556 0.90663355 0.8722969 0.78652835 0.83722969 0.79456458 0.85417884 0.82919345 0.89655172 0.86206897] mean value: 0.8460841613091759 key: train_roc_auc value: [0.91149556 0.90769509 0.91147746 0.90103205 0.92105378 0.90486692 0.91533406 0.91153902 0.91444867 0.91444867] mean value: 0.9113391272858953 key: test_jcc value: [0.72368421 0.83823529 0.7826087 0.65277778 0.73239437 0.67123288 0.76056338 0.70588235 0.8125 0.76470588] mean value: 0.7444584836559234 key: train_jcc value: [0.84020619 0.83304647 0.84290541 0.8225256 0.85714286 0.82847341 0.84602076 0.84129693 0.84693878 0.84615385] mean value: 0.8404710241602029 MCC on Blind test: 0.26 MCC on Training: 0.7 Running classifier: 2 Model_name: Bagging Classifier Model func: BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3))]) key: fit_time value: [0.5684762 0.60643625 0.60138559 0.62040544 0.65591526 0.61065626 0.5730474 0.59461713 0.60937595 0.61642504] [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... 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Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 2.8s remaining: 5.6s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 2.9s remaining: 5.8s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 3.0s remaining: 5.9s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 3.0s remaining: 6.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 3.0s remaining: 1.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 3.0s remaining: 6.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 3.0s remaining: 6.1s Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 3.1s remaining: 6.2s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 3.1s remaining: 6.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 3.1s remaining: 1.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 3.1s remaining: 1.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 3.1s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 3.1s remaining: 1.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 3.2s remaining: 1.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 3.2s remaining: 1.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 3.2s remaining: 6.3s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 3.2s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 3.2s remaining: 1.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 3.2s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 3.2s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 3.2s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 3.2s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 3.2s remaining: 6.4s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 3.2s remaining: 1.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 3.2s remaining: 1.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 3.3s remaining: 1.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 3.3s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 3.3s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 3.3s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 3.3s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.4s remaining: 0.7s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished mean value: 0.6056740522384644 key: score_time value: [0.07736754 0.06542873 0.0661602 0.04965878 0.06403804 0.06641006 0.05278921 0.04442143 0.07703733 0.07270479] mean value: 0.06360161304473877 key: test_mcc value: [0.91794064 0.96638414 0.9022688 0.87156767 0.90210482 0.87128374 0.98304594 1. 0.96609178 0.96609178] mean value: 0.9346779302596779 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.95867769 0.98305085 0.95081967 0.93548387 0.9516129 0.93650794 0.99159664 1. 0.98305085 0.98305085] mean value: 0.9673851249811387 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.92063492 0.96666667 0.90625 0.87878788 0.90769231 0.88059701 0.98333333 1. 0.96666667 0.96666667] mean value: 0.9377295455373813 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.95726496 0.98290598 0.94871795 0.93162393 0.94871795 0.93162393 0.99145299 1. 0.98275862 0.98275862] mean value: 0.9657824933687001 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.95762712 0.98305085 0.94915254 0.93220339 0.94827586 0.93103448 0.99137931 1. 0.98275862 0.98275862] mean value: 0.9658240794856809 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.92063492 0.96666667 0.90625 0.87878788 0.90769231 0.88059701 0.98333333 1. 0.96666667 0.96666667] mean value: 0.9377295455373813 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.27 MCC on Training: 0.93 Running classifier: 3 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.06661701 0.04532051 0.04293752 0.03992033 0.04093719 0.04808879 0.04014087 0.04167795 0.04081082 0.04078913] mean value: 0.044724011421203615 key: score_time value: [0.00896811 0.00893736 0.0090735 0.00913024 0.00896573 0.00925374 0.00896907 0.009058 0.00913763 0.00906062] mean value: 0.009055399894714355 key: test_mcc value: [0.87156767 0.93384219 0.85651622 0.84165009 0.81182375 0.82644112 0.8865947 0.90210482 0.87038828 0.93325653] mean value: 0.873418535666463 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.93548387 0.96666667 0.928 0.92063492 0.90769231 0.91472868 0.944 0.9516129 0.93548387 0.96666667] mean value: 0.9370969888992393 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.87878788 0.93548387 0.86567164 0.85294118 0.83098592 0.84285714 0.89393939 0.90769231 0.87878788 0.93548387] mean value: 0.8822631077754677 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.93162393 0.96581197 0.92307692 0.91452991 0.8974359 0.90598291 0.94017094 0.94871795 0.93103448 0.96551724] mean value: 0.9323902151488358 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.93220339 0.96610169 0.92372881 0.91525424 0.89655172 0.90517241 0.93965517 0.94827586 0.93103448 0.96551724] mean value: 0.9323495032144944 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.87878788 0.93548387 0.86567164 0.85294118 0.83098592 0.84285714 0.89393939 0.90769231 0.87878788 0.93548387] mean value: 0.8822631077754677 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.14 MCC on Training: 0.87 Running classifier: 4 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.01387739 0.01336312 0.01386571 0.01306057 0.01401782 0.01408005 0.01303411 0.01344943 0.01312351 0.01322746] mean value: 0.013509917259216308 key: score_time value: [0.00967646 0.0091331 0.0099268 0.00943017 0.00937271 0.00991678 0.0096693 0.00967741 0.01006293 0.00898027] mean value: 0.009584593772888183 key: test_mcc value: [0.87156767 0.88681491 0.87156767 0.88681491 0.8561613 0.90210482 0.87128374 0.8865947 0.84016805 0.90138782] mean value: 0.8774465574190538 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.93548387 0.94308943 0.93548387 0.94308943 0.92913386 0.9516129 0.93650794 0.944 0.92063492 0.95081967] mean value: 0.938985589449163 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.87878788 0.89230769 0.87878788 0.89230769 0.86764706 0.90769231 0.88059701 0.89393939 0.85294118 0.90625 ] mean value: 0.8851258094042335 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.93162393 0.94017094 0.93162393 0.94017094 0.92307692 0.94871795 0.93162393 0.94017094 0.9137931 0.94827586] mean value: 0.9349248452696729 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.93220339 0.94067797 0.93220339 0.94067797 0.92241379 0.94827586 0.93103448 0.93965517 0.9137931 0.94827586] mean value: 0.9349210987726476 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.87878788 0.89230769 0.87878788 0.89230769 0.86764706 0.90769231 0.88059701 0.89393939 0.85294118 0.90625 ] mean value: 0.8851258094042335 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.08 MCC on Training: 0.88 Running classifier: 5 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.17516994 0.18148136 0.18065548 0.17636347 0.17475867 0.17354298 0.18375087 0.18300271 0.18114567 0.18532419] mean value: 0.17951953411102295 key: score_time value: [0.02012801 0.0191884 0.01926804 0.01890111 0.01891589 0.02052736 0.01999497 0.01888013 0.01903152 0.01993418] mean value: 0.019476962089538575 key: test_mcc value: [0.96638414 0.94998574 1. 0.96638414 0.96636481 0.9337672 1. 1. 0.98290472 0.98290472] mean value: 0.9748695472217712 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.98305085 0.97478992 1. 0.98305085 0.98333333 0.96721311 1. 1. 0.99145299 0.99145299] mean value: 0.9874344041875055 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.96666667 0.95081967 1. 0.96666667 0.96721311 0.93650794 1. 1. 0.98305085 0.98305085] mean value: 0.9753975751641768 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.98290598 0.97435897 1. 0.98290598 0.98290598 0.96581197 1. 1. 0.99137931 0.99137931] mean value: 0.9871647509578544 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.98305085 0.97457627 1. 0.98305085 0.98275862 0.96551724 1. 1. 0.99137931 0.99137931] mean value: 0.9871712448860315 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.96666667 0.95081967 1. 0.96666667 0.96721311 0.93650794 1. 1. 0.98305085 0.98305085] mean value: 0.9753975751641768 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.07 MCC on Training: 0.97 Running classifier: 6 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [1.2507422 1.26290202 1.22202802 1.21145725 1.21948361 1.22325277 1.21567082 1.21688962 1.20343828 1.21389222] mean value: 1.2239756822586059 key: score_time value: [0.0093317 0.01009536 0.00949597 0.0093677 0.00975347 0.00996375 0.00969386 0.00946784 0.0093534 0.00943995] mean value: 0.009596300125122071 key: test_mcc value: [0.88681491 0.93384219 0.88681491 0.81243364 0.91782516 0.87128374 0.94994292 0.98304594 0.90138782 0.91720763] mean value: 0.9060598847706656 key: train_mcc value: [0.97555963 0.98301734 0.98301734 0.98301734 0.98301789 0.98114849 0.97556076 0.97928251 0.97930162 0.97930162] mean value: 0.9802224546404309 key: test_fscore value: [0.94308943 0.96666667 0.94308943 0.90625 0.95934959 0.93650794 0.97520661 0.99159664 0.95081967 0.95867769] mean value: 0.9531253666766426 key: train_fscore value: [0.98779343 0.99151744 0.99151744 0.99151744 0.99150142 0.99056604 0.98777046 0.98963242 0.98965193 0.98965193] mean value: 0.9901119930749992 key: test_precision value: [0.89230769 0.93548387 0.89230769 0.82857143 0.921875 0.88059701 0.9516129 0.98333333 0.90625 0.92063492] mean value: 0.9112973856273989 key: train_precision value: [0.97588126 0.98317757 0.98317757 0.98317757 0.98314607 0.98130841 0.97583643 0.97947761 0.97951583 0.97951583] mean value: 0.9804214151029349 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.94017094 0.96581197 0.94017094 0.8974359 0.95726496 0.93162393 0.97435897 0.99145299 0.94827586 0.95689655] mean value: 0.9503463012083703 key: train_accuracy value: [0.98763083 0.99143673 0.99143673 0.99143673 0.99143673 0.99048525 0.98763083 0.98953378 0.98954373 0.98954373] mean value: 0.9900115045240275 key: test_roc_auc value: [0.94067797 0.96610169 0.94067797 0.89830508 0.95689655 0.93103448 0.97413793 0.99137931 0.94827586 0.95689655] mean value: 0.9504383401519579 key: train_roc_auc value: [0.98761905 0.99142857 0.99142857 0.99142857 0.99144487 0.9904943 0.98764259 0.98954373 0.98954373 0.98954373] mean value: 0.9900117689661416 key: test_jcc value: [0.89230769 0.93548387 0.89230769 0.82857143 0.921875 0.88059701 0.9516129 0.98333333 0.90625 0.92063492] mean value: 0.9112973856273989 key: train_jcc value: [0.97588126 0.98317757 0.98317757 0.98317757 0.98314607 0.98130841 0.97583643 0.97947761 0.97951583 0.97951583] mean value: 0.9804214151029349 MCC on Blind test: 0.39 MCC on Training: 0.91 Running classifier: 7 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.01251841 0.01345205 0.01348138 0.01369643 0.01318741 0.01188064 0.01197267 0.01326323 0.01294112 0.01272678] mean value: 0.01291201114654541 key: score_time value: [0.00983214 0.01044226 0.00961041 0.01025295 0.00989532 0.00940704 0.00923395 0.00942349 0.01021957 0.01011038] mean value: 0.009842753410339355 key: test_mcc value: [0.43786823 0.40163697 0.50511865 0.3218579 0.36928646 0.4191757 0.34040776 0.42008262 0.43109856 0.31201886] mean value: 0.39585517122874936 key: train_mcc value: [0.39142298 0.46365728 0.41808661 0.42020964 0.41415312 0.43302111 0.4046613 0.43682809 0.42088367 0.41255424] mean value: 0.42154780567363126 key: test_fscore value: [0.72727273 0.69565217 0.75630252 0.61538462 0.67256637 0.70689655 0.63551402 0.70175439 0.71794872 0.67213115] mean value: 0.6901423231130546 key: train_fscore value: [0.68932039 0.72727273 0.70291262 0.66950959 0.71161049 0.71290944 0.70720299 0.71483622 0.70127326 0.70543375] mean value: 0.7042281484880781 key: test_precision value: [0.6984127 0.70175439 0.73770492 0.69565217 0.7037037 0.71929825 0.70833333 0.72727273 0.71186441 0.640625 ] mean value: 0.7044621593026902 key: train_precision value: [0.70436508 0.74015748 0.71825397 0.76213592 0.69981584 0.72124756 0.69485294 0.72319688 0.72323232 0.70745698] mean value: 0.7194714976022224 key: test_recall value: [0.75862069 0.68965517 0.77586207 0.55172414 0.6440678 0.69491525 0.57627119 0.6779661 0.72413793 0.70689655] mean value: 0.6800116890707188 key: train_recall value: [0.67490494 0.7148289 0.68821293 0.59695817 0.72380952 0.7047619 0.72 0.70666667 0.68060837 0.70342205] mean value: 0.6914173456454826 key: test_accuracy value: [0.71794872 0.7008547 0.75213675 0.65811966 0.68376068 0.70940171 0.66666667 0.70940171 0.71551724 0.65517241] mean value: 0.6968980253463012 key: train_accuracy value: [0.69552807 0.73168411 0.70884872 0.70504282 0.70694577 0.71646051 0.70218839 0.71836346 0.71007605 0.70627376] mean value: 0.7101411655747016 key: test_roc_auc value: [0.7182934 0.70075979 0.75233781 0.657218 0.68410286 0.70952659 0.66744594 0.70967271 0.71551724 0.65517241] mean value: 0.6970046756282875 key: train_roc_auc value: [0.69554771 0.73170016 0.70886837 0.70514575 0.7069618 0.71644939 0.70220532 0.71835234 0.71007605 0.70627376] mean value: 0.7101580662683323 key: test_jcc value: [0.57142857 0.53333333 0.60810811 0.44444444 0.50666667 0.54666667 0.46575342 0.54054054 0.56 0.50617284] mean value: 0.5283114595352038 key: train_jcc value: [0.52592593 0.57142857 0.54191617 0.50320513 0.55232558 0.55389222 0.54703329 0.55622189 0.53996983 0.544919 ] mean value: 0.5436837596952773 MCC on Blind test: 0.26 MCC on Training: 0.4 Running classifier: 8 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.60729742 0.5124042 0.53412986 0.51716638 0.48492956 0.48587036 0.52165198 0.65298438 0.60661769 0.4974072 ] mean value: 0.5420459032058715 key: score_time value: [0.04381347 0.04514027 0.04526663 0.05056691 0.04510474 0.04113984 0.02632117 0.04623103 0.04483676 0.04290962] mean value: 0.04313304424285889 key: test_mcc value: [0.79806402 0.73770245 0.89798308 0.75284822 0.78317338 0.67183351 0.89947631 0.76231277 0.85518611 0.84595998] mean value: 0.8004539843024299 key: train_mcc value: [0.94025435 0.93794951 0.93033133 0.93573163 0.92987703 0.93554501 0.93627935 0.92639313 0.93618042 0.93633638] mean value: 0.9344878141262232 key: test_fscore value: [0.89922481 0.87301587 0.94915254 0.88 0.896 0.84615385 0.95081967 0.8852459 0.928 0.92436975] mean value: 0.9031982389413804 key: train_fscore value: [0.9703154 0.96924511 0.96551724 0.96816479 0.96519285 0.96798493 0.96834264 0.96351731 0.96834264 0.96840149] mean value: 0.967502440748253 key: test_precision value: [0.81690141 0.80882353 0.93333333 0.82089552 0.84848485 0.77464789 0.92063492 0.85714286 0.86567164 0.90163934] mean value: 0.8548175293223773 key: train_precision value: [0.94746377 0.95063985 0.94698355 0.95387454 0.9535316 0.95716946 0.94717668 0.94669118 0.94890511 0.94727273] mean value: 0.9499708463816697 key: test_recall value: [1. 0.94827586 0.96551724 0.94827586 0.94915254 0.93220339 0.98305085 0.91525424 1. 0.94827586] mean value: 0.9590005844535361 key: train_recall value: [0.99429658 0.98859316 0.98479087 0.98288973 0.97714286 0.97904762 0.99047619 0.98095238 0.98859316 0.9904943 ] mean value: 0.9857276842295853 key: test_accuracy value: [0.88888889 0.86324786 0.94871795 0.87179487 0.88888889 0.82905983 0.94871795 0.88034188 0.92241379 0.92241379] mean value: 0.8964485705865016 key: train_accuracy value: [0.96955281 0.96860133 0.96479543 0.96764986 0.96479543 0.96764986 0.96764986 0.96289248 0.96768061 0.96768061] mean value: 0.966894827667295 key: test_roc_auc value: [0.88983051 0.86396844 0.94886032 0.87244302 0.88836937 0.82817066 0.94842198 0.88004091 0.92241379 0.92241379] mean value: 0.8964932787843367 key: train_roc_auc value: [0.96952924 0.96858229 0.96477639 0.96763534 0.96480717 0.96766069 0.96767156 0.96290965 0.96768061 0.96768061] mean value: 0.9668933550606553 key: test_jcc value: [0.81690141 0.77464789 0.90322581 0.78571429 0.8115942 0.73333333 0.90625 0.79411765 0.86567164 0.859375 ] mean value: 0.8250831213022298 key: train_jcc value: [0.94234234 0.9403255 0.93333333 0.93829401 0.93272727 0.9379562 0.93862816 0.92960289 0.93862816 0.93873874] mean value: 0.9370576605474238 MCC on Blind test: 0.08 MCC on Training: 0.8 Running classifier: 9 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.0153904 0.0115571 0.0118773 0.01071262 0.01061487 0.01068854 0.01122832 0.01119089 0.01107144 0.01101398] mean value: 0.011534547805786133 key: score_time value: [0.04139209 0.01642919 0.01583815 0.01573205 0.01568723 0.01871157 0.0157671 0.01635861 0.01627278 0.02087855] mean value: 0.01930673122406006 key: test_mcc value: [0.52723726 0.60649937 0.76814635 0.67069032 0.67691327 0.59831245 0.68696242 0.69194244 0.63355259 0.63441664] mean value: 0.6494673112169634 key: train_mcc value: [0.78357705 0.77941237 0.77711948 0.79136621 0.79144448 0.79211772 0.77102102 0.77468782 0.78886391 0.7757533 ] mean value: 0.7825363354705982 key: test_fscore value: [0.7826087 0.81481481 0.88709677 0.84210526 0.84671533 0.81428571 0.85271318 0.85496183 0.82608696 0.828125 ] mean value: 0.8349513557448681 key: train_fscore value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( [0.89423904 0.89212329 0.89117395 0.89767842 0.89750215 0.89767842 0.88831615 0.88964927 0.89590444 0.89041096] mean value: 0.8934676083879893 key: test_precision value: [0.675 0.71428571 0.83333333 0.74666667 0.74358974 0.7037037 0.78571429 0.77777778 0.7125 0.75714286] mean value: 0.7449714082214083 key: train_precision value: [0.81632653 0.81152648 0.81123245 0.81946625 0.81918239 0.81818182 0.80907668 0.80745342 0.8126935 0.80996885] mean value: 0.8135108360086821 key: test_recall value: [0.93103448 0.94827586 0.94827586 0.96551724 0.98305085 0.96610169 0.93220339 0.94915254 0.98275862 0.9137931 ] mean value: 0.9520163646990063 key: train_recall value: [0.98859316 0.9904943 0.98859316 0.99239544 0.99238095 0.99428571 0.9847619 0.99047619 0.99809886 0.98859316] mean value: 0.9908672822741265 key: test_accuracy value: [0.74358974 0.78632479 0.88034188 0.82051282 0.82051282 0.77777778 0.83760684 0.83760684 0.79310345 0.81034483] mean value: 0.8107721780135574 key: train_accuracy value: [0.8829686 0.88011418 0.8791627 0.8867745 0.8867745 0.8867745 0.87630828 0.87725975 0.88403042 0.878327 ] mean value: 0.8818494426817841 key: test_roc_auc value: [0.74517826 0.78769725 0.88091759 0.82174167 0.81911163 0.7761543 0.83679135 0.83664524 0.79310345 0.81034483] mean value: 0.8107685563997663 key: train_roc_auc value: [0.88286801 0.88000905 0.87905848 0.88667391 0.88687489 0.8868767 0.87641137 0.87736737 0.88403042 0.878327 ] mean value: 0.8818497193554228 key: test_jcc value: [0.64285714 0.6875 0.79710145 0.72727273 0.73417722 0.68674699 0.74324324 0.74666667 0.7037037 0.70666667] mean value: 0.7175935802827194 key: train_jcc value: [0.80870918 0.80525502 0.80370943 0.81435257 0.8140625 0.81435257 0.79907264 0.80123267 0.8114374 0.80246914] mean value: 0.8074653123068174 MCC on Blind test: -0.01 MCC on Training: 0.65 Running classifier: 10 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.05713463 0.10427809 0.09590697 0.09334874 0.07382441 0.06287456 0.08844018 0.08520293 0.07196546 0.09256196] mean value: 0.0825537919998169 key: score_time value: [0.01922989 0.02700567 0.02067018 0.02046847 0.01261806 0.01234007 0.01308203 0.01242614 0.01632571 0.01952982] mean value: 0.017369604110717772 key: test_mcc value: [0.52272202 0.50645001 0.69919881 0.69622043 0.5789676 0.47043398 0.59085834 0.62514923 0.65673607 0.70953798] mean value: 0.6056274460750902 key: train_mcc value: [0.75322025 0.7148123 0.73895425 0.73750523 0.7496814 0.74059993 0.72885385 0.75429643 0.73058218 0.71401376] mean value: 0.7362519573543396 key: test_fscore value: [0.77862595 0.76033058 0.85483871 0.85245902 0.80314961 0.74380165 0.80327869 0.81967213 0.83333333 0.85950413] mean value: 0.8108993803210376 key: train_fscore value: [0.8803653 0.86184812 0.87398005 0.87259395 0.87853881 0.87326549 0.86722377 0.88101726 0.86940639 0.86031452] mean value: 0.8718553676883684 key: test_precision value: [0.69863014 0.73015873 0.8030303 0.8125 0.75 0.72580645 0.77777778 0.79365079 0.80645161 0.82539683] mean value: 0.772340263151686 key: train_precision value: [0.84710018 0.83068783 0.83535529 0.84247788 0.84385965 0.84892086 0.84601449 0.84201389 0.83655536 0.83783784] mean value: 0.8410823260696526 key: test_recall value: [0.87931034 0.79310345 0.9137931 0.89655172 0.86440678 0.76271186 0.83050847 0.84745763 0.86206897 0.89655172] mean value: 0.854646405610754 key: train_recall value: [0.91634981 0.89543726 0.91634981 0.90494297 0.91619048 0.89904762 0.88952381 0.92380952 0.90494297 0.88403042] mean value: 0.9050624660510593 key: test_accuracy value: [0.75213675 0.75213675 0.84615385 0.84615385 0.78632479 0.73504274 0.79487179 0.81196581 0.82758621 0.85344828] mean value: 0.8005820807544947 key: train_accuracy value: [0.8753568 0.85632731 0.867745 0.867745 0.87345385 0.86964795 0.86393911 0.8753568 0.86406844 0.85646388] mean value: 0.8670104155737972 key: test_roc_auc value: [0.75321449 0.75248393 0.84672706 0.84658095 0.78565167 0.73480421 0.79456458 0.81165985 0.82758621 0.85344828] mean value: 0.8006721215663355 key: train_roc_auc value: [0.87531776 0.85629006 0.86769871 0.86770958 0.87349448 0.8696759 0.86396343 0.87540286 0.86406844 0.85646388] mean value: 0.8670085098678255 key: test_jcc value: [0.6375 0.61333333 0.74647887 0.74285714 0.67105263 0.59210526 0.67123288 0.69444444 0.71428571 0.75362319] mean value: 0.6836913468015039 key: train_jcc value: [0.7862969 0.75723473 0.77616747 0.77398374 0.78338762 0.77504105 0.76557377 0.78733766 0.76898223 0.75487013] mean value: 0.7728875303989358 MCC on Blind test: 0.12 MCC on Training: 0.61 Running classifier: 11 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.05438185 0.09056115 0.07722497 0.08015156 0.08336854 0.06190968 0.04630542 0.04526424 0.04725337 0.04836035] mean value: 0.06347811222076416 key: score_time value: [0.0121572 0.01425624 0.01256084 0.02359986 0.01828289 0.01261926 0.01218677 0.01226425 0.01220465 0.01220584] mean value: 0.014233779907226563 key: test_mcc value: [0.50645001 0.52227657 0.69408772 0.55885484 0.56027975 0.31617444 0.55571826 0.47398728 0.53456222 0.60353799] mean value: 0.5325929074416667 key: train_mcc value: [0.65457524 0.62712836 0.65838675 0.69071617 0.62538359 0.64035459 0.62531878 0.64870439 0.63707673 0.6608445 ] mean value: 0.6468489098386305 key: test_fscore value: [0.76033058 0.75 0.85 0.78688525 0.79365079 0.66666667 0.78333333 0.72072072 0.76521739 0.8034188 ] mean value: 0.7680223533508702 key: train_fscore value: [0.83148148 0.81544256 0.83333333 0.8489342 0.81502347 0.82051282 0.81467545 0.82790698 0.82065728 0.83471837] mean value: 0.8262685944515715 key: test_precision value: [0.73015873 0.77777778 0.82258065 0.75 0.74626866 0.6557377 0.7704918 0.76923077 0.77192982 0.79661017] mean value: 0.7590786081294635 key: train_precision value: [0.81046931 0.80783582 0.81227437 0.82820976 0.8037037 0.81818182 0.80483271 0.80909091 0.81076067 0.81149013] mean value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( 0.8116849206432468 key: test_recall value: [0.79310345 0.72413793 0.87931034 0.82758621 0.84745763 0.6779661 0.79661017 0.6779661 0.75862069 0.81034483] mean value: 0.7793103448275861 key: train_recall value: [0.85361217 0.82319392 0.85551331 0.87072243 0.82666667 0.82285714 0.8247619 0.84761905 0.83079848 0.85931559] mean value: 0.8415060655440882 key: test_accuracy value: [0.75213675 0.76068376 0.84615385 0.77777778 0.77777778 0.65811966 0.77777778 0.73504274 0.76724138 0.80172414] mean value: 0.7654435602711465 key: train_accuracy value: [0.82683159 0.81351094 0.82873454 0.84490961 0.81255947 0.82017127 0.81255947 0.82397716 0.81844106 0.82984791] mean value: 0.823154301715187 key: test_roc_auc value: [0.75248393 0.76037405 0.84643483 0.77819988 0.77717709 0.65794857 0.77761543 0.73553477 0.76724138 0.80172414] mean value: 0.7654734073641146 key: train_roc_auc value: [0.82680608 0.81350172 0.82870903 0.84488503 0.81257288 0.82017382 0.81257107 0.82399964 0.81844106 0.82984791] mean value: 0.8231508238276299 key: test_jcc value: [0.61333333 0.6 0.73913043 0.64864865 0.65789474 0.5 0.64383562 0.56338028 0.61971831 0.67142857] mean value: 0.625736993302292 key: train_jcc value: [0.71156894 0.68839428 0.71428571 0.73752013 0.68779715 0.69565217 0.68730159 0.70634921 0.69585987 0.7163233 ] mean value: 0.7041052341848503 MCC on Blind test: 0.21 MCC on Training: 0.53 Running classifier: 12 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(cv=3, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(cv=3, random_state=42))]) key: fit_time value: [0.63260627 0.68174744 0.69094586 0.6667788 0.63811874 0.7223711 0.65061021 0.66257834 0.81514812 0.63474846] mean value: 0.6795653343200684 key: score_time value: [0.01229286 0.01235676 0.01241136 0.01224399 0.01233435 0.01237321 0.01229191 0.01231551 0.01228118 0.01289845] mean value: 0.012379956245422364 key: test_mcc value: [0.54279188 0.50838958 0.65983708 0.59133581 0.60657471 0.50738321 0.68696242 0.62390415 0.65673607 0.67492637] mean value: 0.60588412808754 key: train_mcc value: [0.74209539 0.72856485 0.74455593 0.74774275 0.76330665 0.73479953 0.75621377 0.74988829 0.74051995 0.71814474] mean value: 0.7425831856531679 key: test_fscore value: [0.7804878 0.76422764 0.83333333 0.8 0.81818182 0.768 0.85271318 0.81355932 0.83333333 0.84297521] mean value: 0.8106811638942999 key: train_fscore value: [0.87384045 0.86697674 0.87557604 0.87650882 0.88415199 0.87037037 0.88110599 0.87755102 0.87303058 0.86267281] mean value: 0.8741784816201473 key: test_precision value: [0.73846154 0.72307692 0.80645161 0.77419355 0.73972603 0.72727273 0.78571429 0.81355932 0.80645161 0.80952381] mean value: 0.7724431407673991 key: train_precision value: [0.85326087 0.84881603 0.84973166 0.85662432 0.86101083 0.84684685 0.85357143 0.85533454 0.8517179 0.8372093 ] mean value: 0.8514123731111928 key: test_recall value: [0.82758621 0.81034483 0.86206897 0.82758621 0.91525424 0.81355932 0.93220339 0.81355932 0.86206897 0.87931034] mean value: 0.854354178842782 key: train_recall value: [0.89543726 0.88593156 0.90304183 0.8973384 0.90857143 0.8952381 0.91047619 0.90095238 0.89543726 0.88973384] mean value: 0.8982158247329352 key: test_accuracy value: [0.76923077 0.75213675 0.82905983 0.79487179 0.79487179 0.75213675 0.83760684 0.81196581 0.82758621 0.8362069 ] mean value: 0.8005673445328616 key: train_accuracy value: [0.87059943 0.86393911 0.8715509 0.87345385 0.88106565 0.86679353 0.87725975 0.87440533 0.86977186 0.85836502] mean value: 0.8707204436839078 key: test_roc_auc value: [0.76972531 0.75263004 0.82933957 0.79514904 0.79383402 0.75160725 0.83679135 0.81195207 0.82758621 0.8362069 ] mean value: 0.8004821741671538 key: train_roc_auc value: [0.87057577 0.86391816 0.87152091 0.87343111 0.8810918 0.86682057 0.87729133 0.87443056 0.86977186 0.85836502] mean value: 0.8707217092160058 key: test_jcc value: [0.64 0.61842105 0.71428571 0.66666667 0.69230769 0.62337662 0.74324324 0.68571429 0.71428571 0.72857143] mean value: 0.6826872421082948 key: train_jcc value: [0.77594728 0.76518883 0.77868852 0.78016529 0.7923588 0.7704918 0.78747941 0.78181818 0.77467105 0.75850891] mean value: 0.776531809244898 MCC on Blind test: 0.09 MCC on Training: 0.61 Running classifier: 13 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [4.77080369 4.72006917 3.96663356 4.62617517 4.65837169 2.96756768 4.7455194 4.12526512 4.6623311 5.28842044] mean value: 4.453115701675415 key: score_time value: [0.01269197 0.01283884 0.01274228 0.02024531 0.01295328 0.01265836 0.01348639 0.01357579 0.013901 0.01281977] mean value: 0.013791298866271973 key: test_mcc value: [0.82695916 0.91466978 0.85651622 0.82695916 0.8561613 0.73337361 0.91782516 0.88144164 0.88578518 0.93325653] mean value: 0.8632947739238936 key: train_mcc value: [0.99053 0.9961941 0.97185098 0.99241689 0.98489074 0.93502863 0.99620133 0.97154229 0.97930162 0.98116592] mean value: 0.97991224837874 key: test_fscore value: [0.91338583 0.95726496 0.928 0.91338583 0.92913386 0.87218045 0.95934959 0.94214876 0.94308943 0.96666667] mean value: 0.9324605371591289 key: train_fscore value: [0.99526963 0.99809886 0.98594189 0.99621212 0.99243856 0.96762257 0.99809886 0.98580889 0.98965193 0.9905838 ] mean value: 0.9899727124793838 key: test_precision value: [0.84057971 0.94915254 0.86567164 0.84057971 0.86764706 0.78378378 0.921875 0.91935484 0.89230769 0.93548387] mean value: 0.8816435849046206 key: train_precision value: [0.9905838 0.99809886 0.97227357 0.99245283 0.98499062 0.94064748 0.99620493 0.97932331 0.97951583 0.98134328] mean value: 0.9815434516383341 key: test_recall value: [1. 0.96551724 1. 1. 1. 0.98305085 1. 0.96610169 1. 1. ] mean value: 0.9914669783752192 key: train_recall value: [1. 0.99809886 1. 1. 1. 0.99619048 1. 0.99238095 1. 1. ] mean value: 0.9986670287887017 key: test_accuracy value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( [0.90598291 0.95726496 0.92307692 0.90598291 0.92307692 0.85470085 0.95726496 0.94017094 0.93965517 0.96551724] mean value: 0.9272693781314472 key: train_accuracy value: [0.99524263 0.99809705 0.98572788 0.9961941 0.9923882 0.96669838 0.99809705 0.98572788 0.98954373 0.9904943 ] mean value: 0.9898211191224725 key: test_roc_auc value: [0.90677966 0.95733489 0.92372881 0.90677966 0.92241379 0.85359439 0.95689655 0.9399474 0.93965517 0.96551724] mean value: 0.9272647574517826 key: train_roc_auc value: [0.9952381 0.99809705 0.98571429 0.99619048 0.99239544 0.96672642 0.99809886 0.9857342 0.98954373 0.9904943 ] mean value: 0.9898232844468586 key: test_jcc value: [0.84057971 0.91803279 0.86567164 0.84057971 0.86764706 0.77333333 0.921875 0.890625 0.89230769 0.93548387] mean value: 0.8746135804398444 key: train_jcc value: [0.9905838 0.99620493 0.97227357 0.99245283 0.98499062 0.93727599 0.99620493 0.97201493 0.97951583 0.98134328] mean value: 0.9802860711405241 MCC on Blind test: 0.05 MCC on Training: 0.86 Running classifier: 14 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.01657176 0.01749802 0.01613903 0.01585841 0.01588607 0.01582837 0.01578617 0.01609135 0.01621485 0.01657462] mean value: 0.01624486446380615 key: score_time value: [0.0128417 0.01245689 0.01229692 0.0123136 0.01216364 0.0122025 0.01228189 0.01211858 0.01212168 0.0122118 ] mean value: 0.012300920486450196 key: test_mcc value: [0.5393392 0.38607028 0.54279188 0.38461553 0.29918079 0.35077464 0.49017205 0.43786823 0.46614121 0.4689451 ] mean value: 0.43658989207703647 key: train_mcc value: [0.44438167 0.44118734 0.44446619 0.48239725 0.39313521 0.48810791 0.44296202 0.50142859 0.48866469 0.4619847 ] mean value: 0.4588715560782729 key: test_fscore value: [0.77310924 0.67272727 0.7804878 0.68421053 0.66115702 0.67241379 0.73214286 0.7079646 0.7394958 0.74796748] mean value: 0.717167640242232 key: train_fscore value: [0.7245283 0.71289062 0.71923077 0.74144487 0.70046948 0.74405328 0.72744186 0.75 0.74209012 0.73021926] mean value: 0.7292368570734078 key: test_precision value: [0.75409836 0.71153846 0.73846154 0.69642857 0.64516129 0.68421053 0.77358491 0.74074074 0.72131148 0.70769231] mean value: 0.7173228178225941 key: train_precision value: [0.71910112 0.73293173 0.72762646 0.74144487 0.69074074 0.74334601 0.71090909 0.75143403 0.74854932 0.73231358] mean value: 0.7298396948781698 key: test_recall value: [0.79310345 0.63793103 0.82758621 0.67241379 0.6779661 0.66101695 0.69491525 0.6779661 0.75862069 0.79310345] mean value: 0.7194623027469317 key: train_recall value: [0.73003802 0.69391635 0.71102662 0.74144487 0.71047619 0.7447619 0.7447619 0.74857143 0.73574144 0.72813688] mean value: 0.7288875611080934 key: test_accuracy value: [0.76923077 0.69230769 0.76923077 0.69230769 0.64957265 0.67521368 0.74358974 0.71794872 0.73275862 0.73275862] mean value: 0.717491895078102 key: train_accuracy value: [0.72216936 0.72026641 0.72216936 0.74119886 0.69647954 0.74405328 0.72121789 0.75071361 0.74429658 0.73098859] mean value: 0.729355348699229 key: test_roc_auc value: [0.76943308 0.69184687 0.76972531 0.6921391 0.64932788 0.67533606 0.74400935 0.7182934 0.73275862 0.73275862] mean value: 0.717562828755114 key: train_roc_auc value: [0.72216187 0.72029151 0.72217997 0.74119862 0.69649285 0.74405396 0.72124027 0.75071157 0.74429658 0.73098859] mean value: 0.7293615788520731 key: test_jcc value: [0.63013699 0.50684932 0.64 0.52 0.49382716 0.50649351 0.57746479 0.54794521 0.58666667 0.5974026 ] mean value: 0.5606786226638307 key: train_jcc value: [0.56804734 0.5538695 0.56156156 0.58912387 0.53901734 0.59242424 0.57163743 0.6 0.58993902 0.57507508] mean value: 0.5740695374981037 MCC on Blind test: 0.27 MCC on Training: 0.44 Running classifier: 15 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.01692605 0.01785254 0.01681519 0.01698112 0.01693606 0.0169878 0.01700521 0.01691818 0.01719356 0.01704764] mean value: 0.017066335678100585 key: score_time value: [0.01253581 0.01291704 0.01265478 0.01260948 0.01267719 0.01255465 0.01253629 0.01247573 0.01248598 0.01245022] mean value: 0.012589716911315918 key: test_mcc value: [0.20510731 0.2660668 0.36768207 0.33323591 0.43246402 0.18089732 0.37273223 0.33032117 0.22417125 0.32763491] mean value: 0.304031299249271 key: train_mcc value: [0.42946842 0.31503456 0.35954431 0.39167359 0.42812612 0.38929468 0.37722826 0.35627268 0.3702398 0.38232901] mean value: 0.379921142466657 key: test_fscore value: [0.64122137 0.60550459 0.67256637 0.66086957 0.67307692 0.625 0.66055046 0.60784314 0.60869565 0.66086957] mean value: 0.6416197634539298 key: train_fscore value: [0.72067039 0.65384615 0.66863324 0.66940452 0.70344828 0.68985507 0.67330677 0.66201396 0.66998012 0.68599034] mean value: 0.6797148834174693 key: test_precision value: [0.57534247 0.64705882 0.69090909 0.66666667 0.77777778 0.57971014 0.72 0.72093023 0.61403509 0.66666667] mean value: 0.6659096956508013 key: train_precision value: [0.70620438 0.6614786 0.69246436 0.72767857 0.72857143 0.7 0.70563674 0.69456067 0.70208333 0.69744597] mean value: 0.7016124055735492 key: test_recall value: [0.72413793 0.56896552 0.65517241 0.65517241 0.59322034 0.6779661 0.61016949 0.52542373 0.60344828 0.65517241] mean value: 0.626884862653419 key: train_recall value: [0.73574144 0.64638783 0.64638783 0.61977186 0.68 0.68 0.64380952 0.63238095 0.64068441 0.67490494] mean value: 0.6600068803186675 key: test_accuracy value: [0.5982906 0.63247863 0.68376068 0.66666667 0.70940171 0.58974359 0.68376068 0.65811966 0.61206897 0.6637931 ] mean value: 0.649808429118774 key: train_accuracy value: [0.71455756 0.65746908 0.679353 0.69362512 0.71360609 0.69457659 0.68791627 0.67745005 0.68441065 0.69106464] mean value: 0.6894029043496507 key: test_roc_auc value: [0.5993571 0.63194039 0.68351841 0.66656926 0.71040327 0.58898305 0.68439509 0.65926359 0.61206897 0.6637931 ] mean value: 0.6500292226767972 key: train_roc_auc value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") [0.71453739 0.65747963 0.67938439 0.69369546 0.71357414 0.69456274 0.68787434 0.67740721 0.68441065 0.69106464] mean value: 0.6893990584827087 key: test_jcc value: [0.47191011 0.43421053 0.50666667 0.49350649 0.50724638 0.45454545 0.49315068 0.43661972 0.4375 0.49350649] mean value: 0.47288625269534085 key: train_jcc value: [0.56331878 0.48571429 0.50221566 0.50308642 0.54255319 0.52654867 0.50750751 0.4947839 0.50373692 0.52205882] mean value: 0.515152416056152 MCC on Blind test: 0.16 MCC on Training: 0.3 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.0304482 0.02929521 0.02818513 0.02324224 0.02718663 0.02738476 0.02332973 0.02428818 0.02825546 0.04257655] mean value: 0.02841920852661133 key: score_time value: [0.01228046 0.01238966 0.01236224 0.01230884 0.01236129 0.01402426 0.01228642 0.01301432 0.01226163 0.01315546] mean value: 0.012644457817077636 key: test_mcc value: [0.30504287 0.5095936 0.64599879 0.16599086 0.31653125 0.30339598 0.26082454 0.09246739 0.56972815 0.67736514] mean value: 0.38469385817135 key: train_mcc value: [0.33325165 0.47177155 0.57190732 0.33413786 0.62934873 0.62814336 0.3587754 0.15519755 0.61013175 0.59203523] mean value: 0.4684700405827297 key: test_fscore value: [0.41558442 0.66666667 0.82926829 0.33766234 0.67213115 0.68217054 0.28571429 0.0952381 0.77876106 0.84552846] mean value: 0.5608725300956825 key: train_fscore value: [0.41288433 0.65657742 0.79379562 0.3836858 0.81758653 0.82342502 0.43377001 0.14930556 0.81135531 0.80074143] mean value: 0.6083127030827032 key: test_precision value: [0.84210526 0.88571429 0.78461538 0.68421053 0.65079365 0.62857143 0.90909091 0.75 0.8 0.8 ] mean value: 0.7735101448259343 key: train_precision value: [0.89808917 0.84684685 0.76315789 0.93382353 0.80330882 0.77076412 0.91975309 0.84313725 0.78268551 0.78119349] mean value: 0.8342759729844171 key: test_recall value: [0.27586207 0.53448276 0.87931034 0.22413793 0.69491525 0.74576271 0.16949153 0.05084746 0.75862069 0.89655172] mean value: 0.5229982466393922 key: train_recall value: [0.26806084 0.53612167 0.8269962 0.24144487 0.83238095 0.88380952 0.28380952 0.08190476 0.84220532 0.82129278] mean value: 0.5618026434908564 key: test_accuracy value: [0.61538462 0.73504274 0.82051282 0.56410256 0.65811966 0.64957265 0.57264957 0.51282051 0.78448276 0.8362069 ] mean value: 0.6748894783377541 key: train_accuracy value: [0.61845861 0.71931494 0.7849667 0.61179829 0.81446242 0.81065652 0.62987631 0.53377735 0.80418251 0.79562738] mean value: 0.7123121018186553 key: test_roc_auc value: [0.61250731 0.73334307 0.8210111 0.56122151 0.65780245 0.64874342 0.57612507 0.51680304 0.78448276 0.8362069 ] mean value: 0.6748246639392168 key: train_roc_auc value: [0.61879232 0.71948941 0.78492667 0.612151 0.81447945 0.81072605 0.62954735 0.53334782 0.80418251 0.79562738] mean value: 0.7123269961977186 key: test_jcc value: [0.26229508 0.5 0.70833333 0.203125 0.50617284 0.51764706 0.16666667 0.05 0.63768116 0.73239437] mean value: 0.4284315505914388 key: train_jcc value: [0.2601476 0.48873484 0.6580938 0.23738318 0.6914557 0.69984917 0.27695167 0.08067542 0.6825886 0.66769706] mean value: 0.47435770345317074 MCC on Blind test: 0.13 MCC on Training: 0.38 Running classifier: 17 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.0403235 0.04435873 0.04193211 0.04097414 0.0708096 0.08275557 0.09687757 0.04927397 0.04057741 0.0415585 ] mean value: 0.05494410991668701 key: score_time value: [0.01300907 0.01317739 0.01315403 0.01324248 0.02509284 0.02018642 0.01316261 0.01311326 0.01336861 0.0130055 ] mean value: 0.01505122184753418 key: test_mcc value: [0.75579908 0.87156767 0.96638414 0.91794064 0.9337672 0.8561613 0.90210482 0.8865947 0.82532383 0.84016805] mean value: 0.875581141597419 key: train_mcc value: [0.9089684 0.9107156 0.89854152 0.90374298 0.90724032 0.88310275 0.88823262 0.88139779 0.90383944 0.88661045] mean value: 0.8972391864320658 key: test_fscore value: [0.87878788 0.93548387 0.98305085 0.95867769 0.96721311 0.92913386 0.9516129 0.944 0.91338583 0.92063492] mean value: 0.9381980906817857 key: train_fscore value: [0.95462795 0.955495 0.94945848 0.9520362 0.95367847 0.94170404 0.9442446 0.94086022 0.9520362 0.94349776] mean value: 0.9487638922875912 key: test_precision value: [0.78378378 0.87878788 0.96666667 0.92063492 0.93650794 0.86764706 0.90769231 0.89393939 0.84057971 0.85294118] mean value: 0.8849180833451934 key: train_precision value: [0.91319444 0.91478261 0.90378007 0.90846287 0.91145833 0.88983051 0.89437819 0.88832487 0.90846287 0.89303905] mean value: 0.9025713814240985 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.86324786 0.93162393 0.98290598 0.95726496 0.96581197 0.92307692 0.94871795 0.94017094 0.90517241 0.9137931 ] mean value: 0.9331786030061892 key: train_accuracy value: [0.95242626 0.95337774 0.94671741 0.94957184 0.95147479 0.93815414 0.94100856 0.93720266 0.94961977 0.94011407] mean value: 0.9459667237069167 key: test_roc_auc value: [0.86440678 0.93220339 0.98305085 0.95762712 0.96551724 0.92241379 0.94827586 0.93965517 0.90517241 0.9137931 ] mean value: 0.9332115721800116 key: train_roc_auc value: [0.95238095 0.95333333 0.94666667 0.94952381 0.95152091 0.93821293 0.94106464 0.93726236 0.94961977 0.94011407] mean value: 0.9459699438710846 key: test_jcc value: [0.78378378 0.87878788 0.96666667 0.92063492 0.93650794 0.86764706 0.90769231 0.89393939 0.84057971 0.85294118] mean value: 0.8849180833451934 key: train_jcc value: [0.91319444 0.91478261 0.90378007 0.90846287 0.91145833 0.88983051 0.89437819 0.88832487 0.90846287 0.89303905] mean value: 0.9025713814240985 MCC on Blind test: -0.13 MCC on Training: 0.88 Running classifier: 18 Model_name: Random Forest Model func: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42))]) key: fit_time value: [0.84406924 0.87562871 0.85113978 0.83341479 0.82987309 0.84945869 0.85502195 0.8569057 0.84022617 0.8532455 ] mean value: 0.8488983631134033 key: score_time value: [0.17755389 0.18047833 0.16914535 0.38368964 0.17131948 0.15459609 0.1635766 0.14508581 0.18784595 0.16096091] mean value: 0.1894252061843872 key: test_mcc value: [0.93384219 0.96638414 0.94998574 0.93384219 0.9337672 0.91782516 0.98304594 1. 0.98290472 0.98290472] mean value: 0.9584501997620911 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.96666667 0.98305085 0.97478992 0.96666667 0.96721311 0.95934959 0.99159664 1. 0.99145299 0.99145299] mean value: 0.9792239426568825 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.93548387 0.96666667 0.95081967 0.93548387 0.93650794 0.921875 0.98333333 1. 0.98305085 0.98305085] mean value: 0.9596272045489822 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.96581197 0.98290598 0.97435897 0.96581197 0.96581197 0.95726496 0.99145299 1. 0.99137931 0.99137931] mean value: 0.9786177424108459 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.96610169 0.98305085 0.97457627 0.96610169 0.96551724 0.95689655 0.99137931 1. 0.99137931 0.99137931] mean value: 0.9786382232612507 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.93548387 0.96666667 0.95081967 0.93548387 0.93650794 0.921875 0.98333333 1. 0.98305085 0.98305085] mean value: 0.9596272045489822 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.26 MCC on Training: 0.96 Running classifier: 19 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea...age_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42))]) key: fit_time value: [1.25059128 1.30804086 1.26792073 1.18615365 1.2131753 1.23873234 1.24959993 1.23908114 1.22481918 1.26788235] mean value: 1.2445996761322022 key: score_time value: [0.18853021 0.27846885 0.25633526 0.24968123 0.22424674 0.21522307 0.24063301 0.15996909 0.2399826 0.21862936] mean value: 0.22716994285583497 key: test_mcc value: [0.88681491 0.88047925 0.93218361 0.88681491 0.8865947 0.83672774 0.94884541 0.94886032 0.94954692 0.91720763] mean value: 0.9074075386106568 key: train_mcc value: [0.98864661 0.9866811 0.98858941 0.99053 0.992417 0.98858949 0.98858949 0.98672413 0.99431275 0.99431275] mean value: 0.9899392736674614 key: test_fscore value: [0.94308943 0.94017094 0.96610169 0.94308943 0.944 0.92063492 0.97478992 0.97435897 0.97478992 0.95867769] mean value: 0.9539702909751894 key: train_fscore value: [0.99432892 0.99335233 0.9943074 0.99526963 0.99620493 0.99429658 0.99429658 0.99336493 0.9971564 0.9971564 ] mean value: 0.9949734095190017 key: test_precision value: [0.89230769 0.93220339 0.95 0.89230769 0.89393939 0.86567164 0.96666667 0.98275862 0.95081967 0.92063492] mean value: 0.9247309690298723 key: train_precision value: [0.9887218 0.99240987 0.99242424 0.9905838 0.99243856 0.99240987 0.99240987 0.98867925 0.99432892 0.99432892] mean value: 0.9918735106197273 key: test_recall value: [1. 0.94827586 0.98275862 1. 1. 0.98305085 0.98305085 0.96610169 1. 1. ] mean value: 0.986323787258913 key: train_recall value: [1. 0.99429658 0.99619772 1. 1. 0.99619048 0.99619048 0.99809524 1. 1. ] mean value: 0.9980970487054137 key: test_accuracy value: [0.94017094 0.94017094 0.96581197 0.94017094 0.94017094 0.91452991 0.97435897 0.97435897 0.97413793 0.95689655] mean value: 0.9520778072502211 key: train_accuracy value: [0.99429115 0.99333968 0.99429115 0.99524263 0.9961941 0.99429115 0.99429115 0.99333968 0.99714829 0.99714829] mean value: 0.9949577263008613 key: test_roc_auc value: [0.94067797 0.94023963 0.96595558 0.94067797 0.93965517 0.91393922 0.97428404 0.97443016 0.97413793 0.95689655] mean value: 0.9520894213909994 key: train_roc_auc value: [0.99428571 0.99333877 0.99428934 0.9952381 0.99619772 0.99429296 0.99429296 0.9933442 0.99714829 0.99714829] mean value: 0.9949576317218902 key: test_jcc value: [0.89230769 0.88709677 0.93442623 0.89230769 0.89393939 0.85294118 0.95081967 0.95 0.95081967 0.92063492] mean value: 0.9125293223624329 key: train_jcc value: [0.9887218 0.98679245 0.98867925 0.9905838 0.99243856 0.98865784 0.98865784 0.98681733 0.99432892 0.99432892] mean value: 0.9900006730866666 MCC on Blind test: 0.28 MCC on Training: 0.91 Running classifier: 20 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.0231781 0.04256725 0.04299116 0.04248524 0.0349679 0.04908466 0.0383184 0.04203463 0.04617882 0.04194307] mean value: 0.040374922752380374 key: score_time value: [0.01905656 0.01924992 0.01938605 0.01905417 0.01918817 0.02660871 0.01967335 0.01961136 0.01947784 0.01969552] mean value: 0.020100164413452148 key: test_mcc value: [0.42148926 0.52177728 0.73056182 0.65857939 0.55808179 0.38461553 0.55808179 0.59017834 0.621059 0.65556228] mean value: 0.5699986496740833 key: train_mcc value: [0.68309392 0.69071617 0.72275025 0.71160939 0.69995929 0.70551396 0.67487914 0.6928685 0.66176919 0.69164692] mean value: 0.6934806738533906 key: test_fscore value: [0.72131148 0.76271186 0.86885246 0.83050847 0.79032258 0.7 0.79032258 0.79310345 0.80701754 0.83050847] mean value: 0.7894658901411384 key: train_fscore value: [0.84522706 0.8489342 0.86666667 0.85899814 0.85261194 0.85500468 0.83943662 0.85 0.83270677 0.850046 ] mean value: 0.8499632074168076 key: test_precision value: [0.6875 0.75 0.828125 0.81666667 0.75384615 0.68852459 0.75384615 0.80701754 0.82142857 0.81666667] mean value: 0.7723621346477796 key: train_precision value: [0.82459313 0.82820976 0.82363014 0.83876812 0.83546618 0.84007353 0.82777778 0.82702703 0.82342007 0.82352941] mean value: 0.8292495145727321 key: test_recall value: [0.75862069 0.77586207 0.9137931 0.84482759 0.83050847 0.71186441 0.83050847 0.77966102 0.79310345 0.84482759] mean value: 0.8083576855639976 key: train_recall value: [0.86692015 0.87072243 0.91444867 0.88022814 0.87047619 0.87047619 0.85142857 0.87428571 0.84220532 0.878327 ] mean value: 0.8719518377693284 key: test_accuracy value: [0.70940171 0.76068376 0.86324786 0.82905983 0.77777778 0.69230769 0.77777778 0.79487179 0.81034483 0.82758621] mean value: 0.7843059239610964 key: train_accuracy value: [0.84110371 0.84490961 0.85918173 0.85537583 0.84966698 0.85252141 0.83729781 0.84586108 0.83079848 0.84505703] mean value: 0.8461773686476397 key: test_roc_auc value: [0.70981882 0.76081239 0.86367621 0.82919345 0.7773232 0.6921391 0.7773232 0.79500292 0.81034483 0.82758621] mean value: 0.784322033898305 key: train_roc_auc value: [0.84107912 0.84488503 0.8591291 0.85535216 0.84968676 0.85253848 0.83731124 0.8458881 0.83079848 0.84505703] mean value: 0.8461725511497376 key: test_jcc value: [0.56410256 0.61643836 0.76811594 0.71014493 0.65333333 0.53846154 0.65333333 0.65714286 0.67647059 0.71014493] mean value: 0.6547688367874753 key: train_jcc value: [0.73194222 0.73752013 0.76470588 0.75284553 0.74308943 0.74673203 0.72330097 0.73913043 0.71336554 0.7392 ] mean value: 0.7391832156867977 MCC on Blind test: 0.21 MCC on Training: 0.57 Running classifier: 21 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=3))]) key: fit_time value: [0.11735344 0.06579494 0.12174392 0.06493282 0.10511971 0.16184235 0.15045071 0.06489444 0.07242441 0.11974239] mean value: 0.10442991256713867 key: score_time value: [0.01229644 0.01230478 0.01231503 0.01245689 0.01242089 0.01374412 0.02128577 0.01245475 0.01916766 0.01304364] mean value: 0.014148998260498046 key: test_mcc value: [0.46373689 0.51095623 0.69622043 0.65857939 0.59483575 0.38461553 0.52572375 0.60684013 0.621059 0.70700164] mean value: 0.5769568736468181 key: train_mcc value: [0.73750523 0.74633095 0.72748132 0.74132193 0.72916294 0.72123235 0.72336379 0.73012346 0.72134753 0.69483234] mean value: 0.7272701845022093 key: test_fscore value: [0.75 0.768 0.85245902 0.83050847 0.80952381 0.7 0.77777778 0.80672269 0.81355932 0.85470085] mean value: 0.7963251944081684 key: train_fscore value: [0.87259395 0.8773842 0.86854034 0.87442713 0.86771508 0.86350975 0.86481481 0.86892759 0.86350975 0.85078777] mean value: 0.8672210370147703 key: test_precision value: [0.68571429 0.71641791 0.8125 0.81666667 0.76119403 0.68852459 0.73134328 0.8 0.8 0.84745763] mean value: 0.7659818393544129 key: train_precision value: [0.84247788 0.84 0.83015598 0.84424779 0.84352518 0.8423913 0.84144144 0.83745583 0.84392015 0.83001808] mean value: 0.8395633627326864 key: test_recall value: [0.82758621 0.82758621 0.89655172 0.84482759 0.86440678 0.71186441 0.83050847 0.81355932 0.82758621 0.86206897] mean value: 0.8306545879602572 key: train_recall value: [0.90494297 0.91825095 0.91064639 0.90684411 0.89333333 0.88571429 0.88952381 0.90285714 0.88403042 0.87262357] mean value: 0.8968766974470398 key: test_accuracy value: [0.72649573 0.75213675 0.84615385 0.82905983 0.79487179 0.69230769 0.76068376 0.8034188 0.81034483 0.85344828] mean value: 0.7868921308576482 key: train_accuracy value: [0.867745 0.8715509 0.86203616 0.86964795 0.86393911 0.86013321 0.86108468 0.86393911 0.86026616 0.84695817] mean value: 0.8627300452583635 key: test_roc_auc value: [0.72735243 0.75277615 0.84658095 0.82919345 0.79427236 0.6921391 0.76008182 0.80333139 0.81034483 0.85344828] mean value: 0.7869520748100525 key: train_roc_auc value: [0.86770958 0.87150643 0.86198986 0.86961253 0.86396705 0.86015752 0.86111171 0.8639761 0.86026616 0.84695817] mean value: 0.8627255114973748 key: test_jcc value: [0.6 0.62337662 0.74285714 0.71014493 0.68 0.53846154 0.63636364 0.67605634 0.68571429 0.74626866] mean value: 0.6639243149054046 key: train_jcc value: [0.77398374 0.7815534 0.76762821 0.77687296 0.76633987 0.75980392 0.76182708 0.76823339 0.75980392 0.74032258] mean value: 0.7656369067549631 MCC on Blind test: 0.11 MCC on Training: 0.58 Running classifier: 22 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.08519435 0.06328678 0.06254315 0.06108952 0.06167603 0.07157803 0.06589508 0.06285024 0.06362629 0.07979107] mean value: 0.06775305271148682 key: score_time value: [0.02474928 0.02414989 0.02396584 0.0233202 0.02364802 0.02490926 0.0258739 0.02339292 0.02462053 0.02717042] mean value: 0.0245800256729126 key: test_mcc value: [0.68373622 0.50572841 0.81209819 0.58993881 0.5924841 0.35077464 0.69260044 0.56130372 0.56972815 0.69130113] mean value: 0.6049693811420565 key: train_mcc value: [0.72672147 0.68982075 0.70533795 0.72810719 0.71265617 0.72279178 0.72787879 0.73793683 0.73194049 0.68252061] mean value: 0.7165712022674608 key: test_fscore value: [0.848 0.73873874 0.90598291 0.78947368 0.80645161 0.67241379 0.85 0.76363636 0.78991597 0.83928571] mean value: 0.8003898779247478 key: train_fscore value: [0.866171 0.84490961 0.85053038 0.86236766 0.85632731 0.85797665 0.86367969 0.87102804 0.86609687 0.84080076] mean value: 0.857988797299192 key: test_precision value: [0.79104478 0.77358491 0.89830508 0.80357143 0.76923077 0.68421053 0.83606557 0.82352941 0.7704918 0.87037037] mean value: 0.8020404649827787 key: train_precision value: [0.84727273 0.84571429 0.8630137 0.87329435 0.85551331 0.87673956 0.86450382 0.85504587 0.86527514 0.84321224] mean value: 0.8589584996966959 key: test_recall value: [0.9137931 0.70689655 0.9137931 0.77586207 0.84745763 0.66101695 0.86440678 0.71186441 0.81034483 0.81034483] mean value: 0.8015780245470484 key: train_recall value: [0.88593156 0.84410646 0.83840304 0.85171103 0.85714286 0.84 0.86285714 0.88761905 0.86692015 0.83840304] mean value: 0.857309433279015 key: test_accuracy value: [0.83760684 0.75213675 0.90598291 0.79487179 0.79487179 0.67521368 0.84615385 0.77777778 0.78448276 0.84482759] mean value: 0.801392572944297 key: train_accuracy value: [0.86298763 0.84490961 0.85252141 0.86393911 0.85632731 0.86108468 0.86393911 0.86869648 0.86596958 0.84125475] mean value: 0.8581629662859562 key: test_roc_auc value: [0.83825248 0.75175336 0.90604909 0.7947107 0.79441847 0.67533606 0.84599649 0.778346 0.78448276 0.84482759] mean value: 0.8014172998246639 key: train_roc_auc value: [0.86296578 0.84491037 0.85253485 0.86395075 0.85632808 0.86106464 0.86393808 0.86871447 0.86596958 0.84125475] mean value: 0.8581631359768241 key: test_jcc value: [0.73611111 0.58571429 0.828125 0.65217391 0.67567568 0.50649351 0.73913043 0.61764706 0.65277778 0.72307692] mean value: 0.6716925686498897 key: train_jcc value: [0.76393443 0.73146623 0.73993289 0.75803723 0.74875208 0.75127768 0.76006711 0.77152318 0.7638191 0.72532895] mean value: 0.7514138863274648 MCC on Blind test: 0.24 MCC on Training: 0.6 Running classifier: 23 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.03914785 0.04233074 0.05242419 0.0561111 0.05248022 0.04910231 0.04091024 0.04952216 0.05104947 0.04632425] mean value: 0.04794025421142578 key: score_time value: [0.0111146 0.01222801 0.01279879 0.01975131 0.01271319 0.01282454 0.01288199 0.01282454 0.01458669 0.01274705] mean value: 0.013447070121765136 key: test_mcc value: [ 0.40480274 0.52063465 0.44773604 0.51283924 0.459334 -0.00288952 0.40143138 0.5789676 0.61665481 0.66149509] mean value: 0.4601006039876479 key: train_mcc value: [0.50824083 0.63446252 0.49550803 0.58800223 0.65465729 0.29018724 0.34675648 0.63519527 0.5720541 0.65952685] mean value: 0.5384590850709732 key: test_fscore value: [0.73548387 0.71287129 0.75167785 0.77697842 0.75912409 0.16901408 0.7375 0.80314961 0.82089552 0.83870968] mean value: 0.7105404405916546 key: train_fscore value: [0.7743857 0.80162767 0.77155825 0.80906149 0.83704974 0.32339089 0.71770335 0.82850041 0.80224179 0.8375 ] mean value: 0.7503019302298477 key: test_precision value: [0.58762887 0.8372093 0.61538462 0.66666667 0.66666667 0.5 0.58415842 0.75 0.72368421 0.78787879] mean value: 0.67192775312696 key: train_precision value: [0.63647491 0.86214442 0.64070352 0.70422535 0.76131045 0.91964286 0.55970149 0.73313783 0.69294606 0.78956229] mean value: 0.7299849177696508 key: test_recall value: [0.98275862 0.62068966 0.96551724 0.93103448 0.88135593 0.10169492 1. 0.86440678 0.94827586 0.89655172] mean value: 0.8192285213325541 key: train_recall value: [0.98859316 0.74904943 0.96958175 0.95057034 0.92952381 0.19619048 1. 0.95238095 0.95247148 0.89163498] mean value: 0.8579996378779647 key: test_accuracy value: [0.64957265 0.75213675 0.68376068 0.73504274 0.71794872 0.4957265 0.64102564 0.78632479 0.79310345 0.82758621] mean value: 0.7082228116710876 key: train_accuracy value: [0.71170314 0.81446242 0.71265461 0.77545195 0.81921979 0.58991437 0.60704091 0.80304472 0.76520913 0.8269962 ] mean value: 0.7425697235658235 key: test_roc_auc value: [0.65239626 0.75102279 0.68614845 0.73670368 0.71654004 0.49912332 0.63793103 0.78565167 0.79310345 0.82758621] mean value: 0.7086206896551723 key: train_roc_auc value: [0.71143944 0.81452471 0.71240992 0.77528517 0.81932464 0.58954011 0.60741445 0.80318667 0.76520913 0.8269962 ] mean value: 0.7425330436357052 key: test_jcc value: [0.58163265 0.55384615 0.60215054 0.63529412 0.61176471 0.09230769 0.58415842 0.67105263 0.69620253 0.72222222] mean value: 0.5750631661667215 key: train_jcc value: [0.63183475 0.66893039 0.62807882 0.67934783 0.71976401 0.1928839 0.55970149 0.70721358 0.6697861 0.72043011] mean value: 0.617797096697669 MCC on Blind test: 0.18 MCC on Training: 0.46 Running classifier: 24 Model_name: XGBoost Model func: /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:463: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_CV['source_data'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:490: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_BT['source_data'] = 'BT' XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.198874 0.16579127 0.16634107 0.17279553 0.1644268 0.16360307 0.17210364 0.16561079 0.31498384 0.21190977] mean value: 0.1896439790725708 key: score_time value: [0.01198888 0.0127933 0.01241851 0.01259613 0.01211572 0.0117631 0.01264167 0.01248908 0.01193428 0.01172972] mean value: 0.012247037887573243 key: test_mcc value: [0.93384219 0.98305085 0.94998574 0.91794064 0.9337672 0.9337672 0.98304594 0.96636481 0.94954692 0.98290472] mean value: 0.953421619328686 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.96666667 0.99145299 0.97478992 0.95867769 0.96721311 0.96721311 0.99159664 0.98333333 0.97478992 0.99145299] mean value: 0.9767186368952828 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.93548387 0.98305085 0.95081967 0.92063492 0.93650794 0.93650794 0.98333333 0.96721311 0.95081967 0.98305085] mean value: 0.9547422151883517 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.96581197 0.99145299 0.97435897 0.95726496 0.96581197 0.96581197 0.99145299 0.98290598 0.97413793 0.99137931] mean value: 0.9760389036251105 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.96610169 0.99152542 0.97457627 0.95762712 0.96551724 0.96551724 0.99137931 0.98275862 0.97413793 0.99137931] mean value: 0.9760520163646988 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.93548387 0.98305085 0.95081967 0.92063492 0.93650794 0.93650794 0.98333333 0.96721311 0.95081967 0.98305085] mean value: 0.9547422151883517 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.27 MCC on Training: 0.95 Extracting tts_split_name: 80_20 Total cols in each df: CV df: 8 metaDF: 17 Adding column: Model_name Total cols in bts df: BT_df: 8 First proceeding to rowbind CV and BT dfs: Final output should have: 25 columns Combinig 2 using pd.concat by row ~ rowbind Checking Dims of df to combine: Dim of CV: (24, 8) Dim of BT: (24, 8) 8 Number of Common columns: 8 These are: ['source_data', 'Precision', 'JCC', 'MCC', 'Recall', 'Accuracy', 'F1', 'ROC_AUC'] Concatenating dfs with different resampling methods [WF]: Split type: 80_20 No. of dfs combining: 2 PASS: 2 dfs successfully combined nrows in combined_df_wf: 48 ncols in combined_df_wf: 8 PASS: proceeding to merge metadata with CV and BT dfs Adding column: Model_name ========================================================= SUCCESS: Ran multiple classifiers ======================================================= Input params: Dim of input df: (858, 175) Data type to split: actual Split type: sl target colname: dst_mode oversampling enabled PASS: x_features has no target variable and no dst column Dropped cols: 2 These were: dst_mode and dst No. of cols in input df: 175 No.of cols dropped: 2 No. of columns for x_features: 173 ------------------------------------------------------------- Successfully generated training and test data: Data used: actual Split type: sl Total no. of input features: 173 --------No. of numerical features: 167 --------No. of categorical features: 6 =========================== Resampling: NONE Baseline =========================== Total data size: 315 Train data size: (291, 173) y_train numbers: Counter({0: 234, 1: 57}) Test data size: (24, 173) y_test_numbers: Counter({0: 19, 1: 5}) y_train ratio: 4.105263157894737 y_test ratio: 3.8 ------------------------------------------------------------- Simple Random OverSampling Counter({0: 234, 1: 234}) (468, 173) Simple Random UnderSampling Counter({0: 57, 1: 57}) (114, 173) Simple Combined Over and UnderSampling Counter({0: 234, 1: 234}) (468, 173) SMOTE_NC OverSampling Counter({0: 234, 1: 234}) (468, 173) Generated Resampled data as below: ================================= Resampling: Random oversampling ================================ Train data size: (468, 173) y_train numbers: 468 y_train ratio: 1.0 y_test ratio: 3.8 ================================ Resampling: Random underampling ================================ Train data size: (114, 173) y_train numbers: 114 y_train ratio: 1.0 y_test ratio: 3.8 ================================ Resampling:Combined (over+under) ================================ Train data size: (468, 173) y_train numbers: 468 y_train ratio: 1.0 y_test ratio: 3.8 ============================== Resampling: Smote NC ============================== Train data size: (468, 173) y_train numbers: 468 y_train ratio: 1.0 y_test ratio: 3.8 ------------------------------------------------------------- ============================================================== Running several classification models (n): 24 List of models: ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('Gaussian NB', GaussianNB()) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('K-Nearest Neighbors', KNeighborsClassifier()) ('LDA', LinearDiscriminantAnalysis()) ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(cv=3, random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Multinomial', MultinomialNB()) ('Naive Bayes', BernoulliNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=12, random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Random Forest', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42)) [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. 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Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42)) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=3)) ('SVC', SVC(random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=12, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ================================================================ Running classifier: 1 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.13077068 0.13179636 0.1334703 0.12990522 0.1343739 0.12819982 0.1255188 0.12544489 0.12754083 0.12718391] mean value: 0.12942047119140626 key: score_time value: [0.01547885 0.01610208 0.01601839 0.01484728 0.01499104 0.01484561 0.01477718 0.01530957 0.01491094 0.0153935 ] mean value: 0.015267443656921387 key: test_mcc value: [ 0.2941742 0.14470719 0.08215838 0.03333333 0.19693028 0.19693028 -0.00777087 -0.09652342 0.53629161 0.38554329] mean value: 0.17657742810463808 key: train_mcc value: [0.97556154 1. 0.97594433 0.97594433 0.97558335 0.97558335 0.98780953 0.97558335 0.95101165 0.98780953] mean value: 0.9780830953937312 key: test_fscore value: [0.4 0.25 0.22222222 0.2 0.25 0.25 0.18181818 0. 0.6 0.44444444] mean value: 0.2798484848484849 key: train_fscore value: [0.98 1. 0.98039216 0.98039216 0.98 0.98 0.99009901 0.98 0.95918367 0.99009901] mean value: 0.9820166006996857 key: test_precision value: [0.5 0.33333333 0.25 0.2 0.5 0.5 0.2 0. 0.75 0.66666667] mean value: 0.39 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.33333333 0.2 0.2 0.2 0.16666667 0.16666667 0.16666667 0. 0.5 0.33333333] mean value: 0.22666666666666666 key: train_recall value: [0.96078431 1. 0.96153846 0.96153846 0.96078431 0.96078431 0.98039216 0.96078431 0.92156863 0.98039216] mean value: 0.9648567119155356 key: test_accuracy value: [0.8 0.79310345 0.75862069 0.72413793 0.79310345 0.79310345 0.68965517 0.75862069 0.86206897 0.82758621] mean value: 0.78 key: train_accuracy value: [0.99233716 1. 0.99236641 0.99236641 0.99236641 0.99236641 0.99618321 0.99236641 0.98473282 0.99618321] mean value: 0.9931268462460882 key: test_roc_auc value: [0.625 0.55833333 0.5375 0.51666667 0.5615942 0.5615942 0.49637681 0.47826087 0.72826087 0.64492754] mean value: 0.5708514492753624 key: train_roc_auc value: [0.98039216 1. 0.98076923 0.98076923 0.98039216 0.98039216 0.99019608 0.98039216 0.96078431 0.99019608] mean value: 0.9824283559577678 key: test_jcc value: [0.25 0.14285714 0.125 0.11111111 0.14285714 0.14285714 0.1 0. 0.42857143 0.28571429] mean value: 0.1728968253968254 key: train_jcc value: [0.96078431 1. 0.96153846 0.96153846 0.96078431 0.96078431 0.98039216 0.96078431 0.92156863 0.98039216] mean value: 0.9648567119155356 MCC on Blind test: 0.12 MCC on Training: 0.18 Running classifier: 2 Model_name: Bagging Classifier Model func: BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3))]) key: fit_time value: [0.18333316 0.2439487 0.23369694 0.19239187 0.22667837 0.19080853 0.2046833 0.25458169 0.21646333 0.23135448] mean value: 0.21779403686523438 key: score_time value: [0.05154967 0.0488379 0.07412505 0.03626895 0.06202292 0.0369029 0.06866431 0.0536828 0.07441568 0.04506135] mean value: 0.05515315532684326 key: test_mcc value: [ 0.20044593 0.14470719 -0.08625819 0.2360294 0.37000643 0.1060244 0.19693028 -0.13900961 -0.13900961 0.1060244 ] mean value: 0.09958906290337036 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.25 0.25 0. 0.28571429 0.28571429 0.22222222 0.25 0. 0. 0.22222222] mean value: 0.17658730158730157 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.5 0.33333333 0. 0.5 1. 0.33333333 0.5 0. 0. 0.33333333] mean value: 0.35 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.16666667 0.2 0. 0.2 0.16666667 0.16666667 0.16666667 0. 0. 0.16666667] mean value: 0.12333333333333334 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8 0.79310345 0.79310345 0.82758621 0.82758621 0.75862069 0.79310345 0.72413793 0.72413793 0.75862069] mean value: 0.78 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.5625 0.55833333 0.47916667 0.57916667 0.58333333 0.53985507 0.5615942 0.45652174 0.45652174 0.53985507] mean value: 0.5316847826086957 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.14285714 0.14285714 0. 0.16666667 0.16666667 0.125 0.14285714 0. 0. 0.125 ] mean value: 0.10119047619047619 key: train_jcc value: [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... 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Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... 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Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... 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Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... 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Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... 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Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.8s remaining: 1.6s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.8s remaining: 1.6s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.8s remaining: 1.6s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.9s remaining: 1.7s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.9s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.9s remaining: 0.3s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.9s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.9s remaining: 1.8s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.9s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.9s remaining: 0.3s Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... 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Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... 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Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... 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[Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.11 MCC on Training: 0.1 Running classifier: 3 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.02372885 0.02024031 0.02213335 0.01867247 0.0220654 0.02193069 0.02025318 0.02431941 0.02463865 0.02312088] mean value: 0.02211031913757324 key: score_time value: [0.00895596 0.00893927 0.00892019 0.00889516 0.00984478 0.00991893 0.00995708 0.01009536 0.01025772 0.01040125] mean value: 0.009618568420410156 key: test_mcc value: [-0.07881104 0.14470719 0.03333333 0.05298129 0.44293978 0.20938142 0.30868293 -0.01235133 0.66506217 0.57971014] mean value: 0.23456358989729148 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.15384615 0.25 0.2 0.26666667 0.54545455 0.4 0.46153846 0.25 0.66666667 0.66666667] mean value: 0.38608391608391607 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.14285714 0.33333333 0.2 0.2 0.6 0.33333333 0.42857143 0.2 1. 0.66666667] mean value: 0.41047619047619055 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.16666667 0.2 0.2 0.4 0.5 0.5 0.5 0.33333333 0.5 0.66666667] mean value: 0.3966666666666666 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.63333333 0.79310345 0.72413793 0.62068966 0.82758621 0.68965517 0.75862069 0.5862069 0.89655172 0.86206897] mean value: 0.7391954022988505 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.45833333 0.55833333 0.51666667 0.53333333 0.70652174 0.61956522 0.66304348 0.49275362 0.75 0.78985507] mean value: 0.6088405797101449 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.08333333 0.14285714 0.11111111 0.15384615 0.375 0.25 0.3 0.14285714 0.5 0.5 ] mean value: 0.2559004884004884 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.05 MCC on Training: 0.23 Running classifier: 4 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.00899649 0.00904226 0.00932837 0.00935006 0.01025152 0.01003504 0.01002145 0.00998044 0.01013613 0.01022649] mean value: 0.00973682403564453 key: score_time value: [0.00850701 0.00850248 0.00878358 0.00903916 0.0091269 0.00923228 0.00934243 0.00905061 0.00909495 0.00918031] mean value: 0.00898597240447998 key: test_mcc value: [ 0. 0.01946247 0.33101875 -0.28171808 -0.13900961 -0.05072464 0.19693028 0.04256283 0.38554329 -0.12478415] mean value: 0.037928113821435835 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.18181818 0.25 0.46153846 0. 0. 0.16666667 0.25 0.2 0.44444444 0.14285714] mean value: 0.20973248973248976 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.2 0.18181818 0.375 0. 0. 0.16666667 0.5 0.25 0.66666667 0.125 ] mean value: 0.2465151515151515 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.16666667 0.4 0.6 0. 0. 0.16666667 0.16666667 0.16666667 0.33333333 0.16666667] mean value: 0.21666666666666665 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.7 0.5862069 0.75862069 0.55172414 0.72413793 0.65517241 0.79310345 0.72413793 0.82758621 0.5862069 ] mean value: 0.6906896551724138 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.5 0.5125 0.69583333 0.33333333 0.45652174 0.47463768 0.5615942 0.51811594 0.64492754 0.43115942] mean value: 0.5128623188405798 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.1 0.14285714 0.3 0. 0. 0.09090909 0.14285714 0.11111111 0.28571429 0.07692308] mean value: 0.12503718503718503 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.24 MCC on Training: 0.04 Running classifier: 5 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.10568833 0.11009431 0.11125374 0.10967827 0.11195135 0.11166406 0.10977936 0.10931969 0.11317325 0.10424542] mean value: 0.10968477725982666 key: score_time value: [0.01785851 0.01910615 0.01941824 0.01921892 0.01885247 0.01873136 0.01788735 0.01876616 0.01862645 0.01700568] mean value: 0.01854712963104248 key: test_mcc value: [ 0. -0.15504342 -0.08625819 -0.08625819 0. 0.1060244 -0.20430157 -0.13900961 0.37000643 0.1060244 ] mean value: -0.008881574148748317 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0. 0. 0. 0. 0. 0.22222222 0. 0. 0.28571429 0.22222222] mean value: 0.073015873015873 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0. 0. 0. 0. 0. 0.33333333 0. 0. 1. 0.33333333] mean value: 0.16666666666666666 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0. 0. 0. 0. 0. 0.16666667 0. 0. 0.16666667 0.16666667] mean value: 0.05 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8 0.72413793 0.79310345 0.79310345 0.79310345 0.75862069 0.65517241 0.72413793 0.82758621 0.75862069] mean value: 0.7627586206896553 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.5 0.4375 0.47916667 0.47916667 0.5 0.53985507 0.41304348 0.45652174 0.58333333 0.53985507] mean value: 0.4928442028985508 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0. 0. 0. 0. 0. 0.125 0. 0. 0.16666667 0.125 ] mean value: 0.041666666666666664 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.05 MCC on Training: -0.01 Running classifier: 6 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.42105794 0.44845533 0.43951559 0.44123173 0.44019961 0.43660188 0.43458438 0.42914295 0.43396997 0.43451023] mean value: 0.4359269618988037 key: score_time value: [0.00919366 0.00969243 0.0106647 0.01007724 0.00988579 0.00958848 0.00935888 0.00918269 0.00948572 0.00926638] mean value: 0.009639596939086914 key: test_mcc value: [-0.13363062 -0.15504342 0.14470719 0.14470719 0.19693028 0.1060244 0.21758446 -0.17349448 0.28942722 0.21758446] mean value: 0.08547966770322828 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0. 0. 0.25 0.25 0.25 0.22222222 0.36363636 0. 0.4 0.36363636] mean value: 0.20994949494949497 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0. 0. 0.33333333 0.33333333 0.5 0.33333333 0.4 0. 0.5 0.4 ] mean value: 0.27999999999999997 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0. 0. 0.2 0.2 0.16666667 0.16666667 0.33333333 0. 0.33333333 0.33333333] mean value: 0.1733333333333333 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.73333333 0.72413793 0.79310345 0.79310345 0.79310345 0.75862069 0.75862069 0.68965517 0.79310345 0.75862069] mean value: 0.7595402298850575 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.45833333 0.4375 0.55833333 0.55833333 0.5615942 0.53985507 0.60144928 0.43478261 0.62318841 0.60144928] mean value: 0.537481884057971 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0. 0. 0.14285714 0.14285714 0.14285714 0.125 0.22222222 0. 0.25 0.22222222] mean value: 0.1248015873015873 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.12 MCC on Training: 0.09 Running classifier: 7 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.00873756 0.0088973 0.00906634 0.00894618 0.00902343 0.0090642 0.00925303 0.00912261 0.0093224 0.00962734] mean value: 0.009106040000915527 key: score_time value: [0.00858617 0.00852919 0.00869584 0.00874662 0.00881553 0.00885105 0.00890398 0.00903344 0.00898004 0.00914621] mean value: 0.00882880687713623 key: test_mcc value: [ 0.26382243 0.2857738 -0.00777087 0.01946247 -0.20430157 0.12704195 -0.31524416 0.20938142 0.39338328 0.10975393] mean value: 0.08813026799265164 key: train_mcc value: [0.39293261 0.38556786 0.35028195 0.39081733 0.33341609 0.39116988 0.32497946 0.37499392 0.31272665 0.35090636] mean value: 0.3607792117421245 key: test_fscore value: [0.42857143 0.42857143 0.18181818 0.25 0. 0.35294118 0. 0.4 0.53333333 0.30769231] mean value: 0.2882927856457268 key: train_fscore value: [0.52173913 0.5203252 0.4964539 0.52413793 0.48120301 0.52307692 0.47552448 0.5112782 0.46666667 0.49180328] mean value: 0.5012208712394627 key: test_precision value: [0.375 0.33333333 0.16666667 0.18181818 0. 0.27272727 0. 0.33333333 0.44444444 0.28571429] mean value: 0.23930375180375174 key: train_precision value: [0.46875 0.45070423 0.39325843 0.40860215 0.3902439 0.43037975 0.36956522 0.41463415 0.35353535 0.42253521] mean value: 0.41022083806662335 key: test_recall value: [0.5 0.6 0.2 0.4 0. 0.5 0. 0.5 0.66666667 0.33333333] mean value: 0.37 key: train_recall value: [0.58823529 0.61538462 0.67307692 0.73076923 0.62745098 0.66666667 0.66666667 0.66666667 0.68627451 0.58823529] mean value: 0.6509426847662142 key: test_accuracy value: [0.73333333 0.72413793 0.68965517 0.5862069 0.65517241 0.62068966 0.51724138 0.68965517 0.75862069 0.68965517] mean value: 0.6664367816091955 key: train_accuracy value: [0.78927203 0.77480916 0.72900763 0.73664122 0.73664122 0.76335878 0.71374046 0.7519084 0.69465649 0.76335878] mean value: 0.7453394168055921 key: test_roc_auc value: [0.64583333 0.675 0.49583333 0.5125 0.41304348 0.57608696 0.32608696 0.61956522 0.72463768 0.55797101] mean value: 0.5546557971014493 key: train_roc_auc value: [0.71316527 0.71483516 0.70796703 0.73443223 0.69524208 0.72669826 0.69589258 0.71958926 0.69147849 0.69696125] mean value: 0.7096261607085416 key: test_jcc value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) [0.27272727 0.27272727 0.1 0.14285714 0. 0.21428571 0. 0.25 0.36363636 0.18181818] mean value: 0.17980519480519483 key: train_jcc value: [0.35294118 0.35164835 0.33018868 0.35514019 0.31683168 0.35416667 0.31192661 0.34343434 0.30434783 0.32608696] mean value: 0.33467124756627203 MCC on Blind test: 0.15 MCC on Training: 0.09 Running classifier: 8 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.06084251 0.06836796 0.11460304 0.08573699 0.10891056 0.10581446 0.1006434 0.09544826 0.1263566 0.13418841] mean value: 0.10009121894836426 key: score_time value: [0.02411509 0.02192307 0.01840496 0.02383137 0.03507304 0.03201532 0.03141689 0.03237224 0.03237033 0.03206372] mean value: 0.028358602523803712 key: test_mcc value: [-0.09284767 -0.15504342 -0.08625819 0. 0. 0. -0.17349448 -0.09652342 0. -0.13900961] mean value: -0.07431767890172541 key: train_mcc value: [0.74497464 0.76286415 0.736178 0.80205658 0.70360226 0.75869498 0.74512819 0.74512819 0.74512819 0.71759132] mean value: 0.74613465084049 key: test_fscore value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_fscore value: [0.75609756 0.77647059 0.74698795 0.81818182 0.70886076 0.77108434 0.75609756 0.75609756 0.75609756 0.725 ] mean value: 0.7570975698969848 key: test_precision value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_recall value: [0.60784314 0.63461538 0.59615385 0.69230769 0.54901961 0.62745098 0.60784314 0.60784314 0.60784314 0.56862745] mean value: 0.6099547511312217 key: test_accuracy value: [0.76666667 0.72413793 0.79310345 0.82758621 0.79310345 0.79310345 0.68965517 0.75862069 0.79310345 0.72413793] mean value: 0.7663218390804598 key: train_accuracy value: [0.92337165 0.92748092 0.91984733 0.9389313 0.91221374 0.92748092 0.92366412 0.92366412 0.92366412 0.91603053] mean value: 0.923634874674622 key: test_roc_auc value: [0.47916667 0.4375 0.47916667 0.5 0.5 0.5 0.43478261 0.47826087 0.5 0.45652174] mean value: 0.47653985507246377 key: train_roc_auc value: [0.80392157 0.81730769 0.79807692 0.84615385 0.7745098 0.81372549 0.80392157 0.80392157 0.80392157 0.78431373] mean value: 0.8049773755656109 key: test_jcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_jcc value: [0.60784314 0.63461538 0.59615385 0.69230769 0.54901961 0.62745098 0.60784314 0.60784314 0.60784314 0.56862745] mean value: 0.6099547511312217 MCC on Blind test: 0.41 MCC on Training: -0.07 Running classifier: 9 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.02332973 0.01052999 0.00931764 0.01018739 0.00945449 0.00973058 0.0096221 0.0103662 0.00982833 0.00970244] mean value: 0.011206889152526855 key: score_time value: [0.01914501 0.01324439 0.01235247 0.01534462 0.01618433 0.01293969 0.01203585 0.01431346 0.01386881 0.01211023] mean value: 0.014153885841369628 key: test_mcc value: [ 0. -0.08625819 -0.08625819 -0.124226 0. 0. -0.13900961 -0.09652342 0. 0. ] mean value: -0.053227541577012816 key: train_mcc value: [0.24678857 0.2775495 0.2213993 0.2213993 0.24695157 0.03769551 0.25425026 0.21744402 0.01740451 0.24892288] mean value: 0.19898054108740862 key: test_fscore value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_fscore value: [0.1754386 0.25396825 0.19672131 0.19672131 0.1754386 0.03703704 0.2295082 0.17241379 0.03636364 0.20338983] mean value: 0.16770005636354374 key: test_precision value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_precision value: [0.83333333 0.72727273 0.66666667 0.66666667 0.83333333 0.33333333 0.7 0.71428571 0.25 0.75 ] mean value: 0.6474891774891774 key: test_recall value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_recall value: [0.09803922 0.15384615 0.11538462 0.11538462 0.09803922 0.01960784 0.1372549 0.09803922 0.01960784 0.11764706] mean value: 0.09728506787330317 key: test_accuracy value: [0.8 0.79310345 0.79310345 0.75862069 0.79310345 0.79310345 0.72413793 0.75862069 0.79310345 0.79310345] mean value: 0.78 key: train_accuracy value: [0.81992337 0.82061069 0.8129771 0.8129771 0.82061069 0.80152672 0.82061069 0.81679389 0.79770992 0.82061069] mean value: 0.8144350852563541 key: test_roc_auc value: [0.5 0.47916667 0.47916667 0.45833333 0.5 0.5 0.45652174 0.47826087 0.5 0.5 ] mean value: 0.48514492753623195 key: train_roc_auc value: [0.54663866 0.56978022 0.55054945 0.55054945 0.54664994 0.50506459 0.56151845 0.54428027 0.50269492 0.55408419] mean value: 0.5431810128353199 key: test_jcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_jcc value: [0.09615385 0.14545455 0.10909091 0.10909091 0.09615385 0.01886792 0.12962963 0.09433962 0.01851852 0.11320755] mean value: 0.09305072984318266 MCC on Blind test: 0.22 MCC on Training: -0.05 Running classifier: 10 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.02389836 0.03209472 0.04022074 0.06495523 0.03303695 0.03438711 0.06597161 0.06607771 0.05393577 0.0329113 ] mean value: 0.04474895000457764 key: score_time value: [0.01222348 0.01193023 0.01190519 0.01191401 0.01272607 0.01307178 0.02453423 0.02149749 0.01304102 0.01299596] mean value: 0.014583945274353027 key: test_mcc value: [ 0.16666667 -0.07747247 0.12677314 -0.04413674 0.38554329 0.4465959 -0.26086957 -0.00777087 0.44293978 -0.08917507] mean value: 0.1089094052292608 key: train_mcc value: [0.74322826 0.77646884 0.74764018 0.76290161 0.761295 0.678531 0.7590775 0.76937305 0.7590775 0.761295 ] mean value: 0.7518887947290542 key: test_fscore value: [0.33333333 0.15384615 0.30769231 0.16666667 0.44444444 0.57142857 0. 0.18181818 0.54545455 0.15384615] mean value: 0.28585303585303584 key: train_fscore value: [0.7826087 0.81632653 0.78723404 0.80412371 0.80412371 0.7311828 0.8 0.8 0.8 0.80412371] mean value: 0.7929723198537154 key: test_precision value: [0.33333333 0.125 0.25 0.14285714 0.66666667 0.5 0. 0.2 0.6 0.14285714] mean value: 0.29607142857142854 key: train_precision value: [0.87804878 0.86956522 0.88095238 0.86666667 0.84782609 0.80952381 0.86363636 0.92307692 0.86363636 0.84782609] mean value: 0.865075867928466 key: test_recall value: [0.33333333 0.2 0.4 0.2 0.33333333 0.66666667 0. 0.16666667 0.5 0.16666667] mean value: 0.29666666666666663 key: train_recall value: [0.70588235 0.76923077 0.71153846 0.75 0.76470588 0.66666667 0.74509804 0.70588235 0.74509804 0.76470588] mean value: 0.7328808446455506 key: test_accuracy value: [0.73333333 0.62068966 0.68965517 0.65517241 0.82758621 0.79310345 0.5862069 0.68965517 0.82758621 0.62068966] mean value: 0.704367816091954 key: train_accuracy value: [0.92337165 0.93129771 0.92366412 0.92748092 0.92748092 0.90458015 0.92748092 0.93129771 0.92748092 0.92748092] mean value: 0.9251615922318738 key: test_roc_auc value: [0.58333333 0.45416667 0.575 0.475 0.64492754 0.74637681 0.36956522 0.49637681 0.70652174 0.45289855] mean value: 0.5504166666666667 key: train_roc_auc value: [0.84103641 0.87032967 0.84386447 0.86071429 0.86576526 0.81437599 0.85833101 0.84583217 0.85833101 0.86576526] mean value: 0.8524345545728318 key: test_jcc value: [0.2 0.08333333 0.18181818 0.09090909 0.28571429 0.4 0. 0.1 0.375 0.08333333] mean value: 0.1800108225108225 key: train_jcc value: [0.64285714 0.68965517 0.64912281 0.67241379 0.67241379 0.57627119 0.66666667 0.66666667 0.66666667 0.67241379] mean value: 0.6575147688039503 MCC on Blind test: -0.23 MCC on Training: 0.11 Running classifier: 11 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.03370094 0.03703856 0.03039432 0.03564119 0.06890488 0.05668998 0.06115079 0.04535174 0.03608656 0.0343895 ] mean value: 0.04393484592437744 key: score_time value: [0.01183414 0.01170015 0.01249242 0.01183295 0.01246142 0.01280332 0.01224923 0.01319814 0.01286793 0.01228523] mean value: 0.012372493743896484 key: test_mcc value: [ 0.11111111 0.2360294 -0.08625819 -0.08625819 -0.09652342 0.19693028 0. 0. 0.38554329 0.19693028] mean value: 0.08575045489504114 key: train_mcc value: [0.43787749 0.39236996 0.45917684 0.46960154 0.41826159 0.33158369 0.43812445 0.39796594 0.35487498 0.37597558] mean value: 0.4075812057645359 key: test_fscore value: [0.22222222 0.28571429 0. 0. 0. 0.25 0. 0. 0.44444444 0.25 ] mean value: 0.14523809523809522 key: train_fscore value: [0.43478261 0.36363636 0.4 0.47222222 0.39393939 0.3125 0.43478261 0.3880597 0.29508197 0.34375 ] mean value: 0.3838754865894936 key: test_precision value: [0.33333333 0.5 0. 0. 0. 0.5 0. 0. 0.66666667 0.5 ] mean value: 0.25 key: train_precision value: [0.83333333 0.85714286 1. 0.85 0.86666667 0.76923077 0.83333333 0.8125 0.9 0.84615385] mean value: 0.8568360805860806 key: test_recall value: [0.16666667 0.2 0. 0. 0. 0.16666667 0. 0. 0.33333333 0.16666667] mean value: 0.10333333333333335 key: train_recall value: [0.29411765 0.23076923 0.25 0.32692308 0.25490196 0.19607843 0.29411765 0.25490196 0.17647059 0.21568627] mean value: 0.24939668174962293 key: test_accuracy value: [0.76666667 0.82758621 0.79310345 0.79310345 0.75862069 0.79310345 0.79310345 0.79310345 0.82758621 0.79310345] mean value: 0.7939080459770115 key: train_accuracy value: [0.85057471 0.83969466 0.85114504 0.85496183 0.84732824 0.83206107 0.85114504 0.84351145 0.83587786 0.83969466] mean value: 0.8445994559971922 key: test_roc_auc value: [0.54166667 0.57916667 0.47916667 0.47916667 0.47826087 0.5615942 0.5 0.5 0.64492754 0.5615942 ] mean value: 0.532554347826087 key: train_roc_auc value: [0.63991597 0.61062271 0.625 0.65631868 0.62271164 0.59093021 0.63994982 0.62034198 0.58586563 0.6031038 ] mean value: 0.6194760434250258 key: test_jcc value: [0.125 0.16666667 0. 0. 0. 0.14285714 0. 0. 0.28571429 0.14285714] mean value: 0.0863095238095238 key: train_jcc value: [0.27777778 0.22222222 0.25 0.30909091 0.24528302 0.18518519 0.27777778 0.24074074 0.17307692 0.20754717] mean value: 0.23887017245507813 MCC on Blind test: -0.11 MCC on Training: 0.09 Running classifier: 12 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(cv=3, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(cv=3, random_state=42))]) key: fit_time value: [0.44923329 0.42416668 0.50001383 0.49432993 0.44104743 0.43627739 0.42629623 0.43530393 0.62344432 0.41869879] mean value: 0.46488118171691895 key: score_time value: [0.0122788 0.01221824 0.01218271 0.01222682 0.01225209 0.01227927 0.01222968 0.0122385 0.01221013 0.01236868] mean value: 0.012248492240905762 key: test_mcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_mcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_fscore value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_fscore value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_precision value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_precision value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_recall value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_recall value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_accuracy value: [0.8 0.82758621 0.82758621 0.82758621 0.79310345 0.79310345 0.79310345 0.79310345 0.79310345 0.79310345] mean value: 0.8041379310344828 key: train_accuracy value: [0.8045977 0.80152672 0.80152672 0.80152672 0.80534351 0.80534351 0.80534351 0.80534351 0.80534351 0.80534351] mean value: 0.804123892252347 key: test_roc_auc value: [0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5] mean value: 0.5 key: train_roc_auc value: [0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5] mean value: 0.5 key: test_jcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_jcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 MCC on Blind test: 0.0 MCC on Training: 0.0 Running classifier: 13 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [1.4390142 1.23587894 1.27373099 1.27673554 1.17805195 1.27138114 1.23336148 1.24674082 1.17839575 1.3638792 ] mean value: 1.269717001914978 key: score_time value: [0.01228523 0.02198482 0.01238346 0.01213551 0.01298094 0.01214957 0.01226807 0.01231074 0.01284647 0.01214504] mean value: 0.013348984718322753 key: test_mcc value: [ 0.16666667 -0.20833333 -0.124226 -0.15504342 0. 0.10975393 -0.26086957 0.1060244 0.38554329 0.1060244 ] mean value: 0.012554037721647437 key: train_mcc value: [0.95081697 0.90283018 0.92738489 0.92738489 0.93865972 0.95101165 0.93865972 0.93865972 0.92625919 0.96331844] mean value: 0.9364985356370068 key: test_fscore value: [0.33333333 0. 0. 0. 0. 0.30769231 0. 0.22222222 0.44444444 0.22222222] mean value: 0.15299145299145298 key: train_fscore value: [0.96 0.91666667 0.93877551 0.93877551 0.94845361 0.95918367 0.94845361 0.94845361 0.9375 0.96969697] mean value: 0.9465959154983455 key: test_precision value: [0.33333333 0. 0. 0. 0. 0.28571429 0. 0.33333333 0.66666667 0.33333333] mean value: 0.19523809523809524 key: train_precision value: [0.97959184 1. 1. 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9979591836734695 key: test_recall value: [0.33333333 0. 0. 0. 0. 0.33333333 0. 0.16666667 0.33333333 0.16666667] mean value: 0.13333333333333333 key: train_recall value: [0.94117647 0.84615385 0.88461538 0.88461538 0.90196078 0.92156863 0.90196078 0.90196078 0.88235294 0.94117647] mean value: 0.9007541478129714 key: test_accuracy value: [0.73333333 0.65517241 0.75862069 0.72413793 0.79310345 0.68965517 0.5862069 0.75862069 0.82758621 0.75862069] mean value: 0.7285057471264368 key: train_accuracy value: [0.98467433 0.96946565 0.97709924 0.97709924 0.98091603 0.98473282 0.98091603 0.98091603 0.97709924 0.98854962] mean value: 0.9801468222631687 key: test_roc_auc value: [0.58333333 0.39583333 0.45833333 0.4375 0.5 0.55797101 0.36956522 0.53985507 0.64492754 0.53985507] mean value: 0.5027173913043479 key: train_roc_auc value: [0.96820728 0.92307692 0.94230769 0.94230769 0.95098039 0.96078431 0.95098039 0.95098039 0.94117647 0.97058824] mean value: 0.9501389786683904 key: test_jcc value: [0.2 0. 0. 0. 0. 0.18181818 0. 0.125 0.28571429 0.125 ] mean value: 0.09175324675324675 key: train_jcc value: [0.92307692 0.84615385 0.88461538 0.88461538 0.90196078 0.92156863 0.90196078 0.90196078 0.88235294 0.94117647] mean value: 0.8989441930618401 MCC on Blind test: -0.01 MCC on Training: 0.01 Running classifier: 14 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.01243901 0.01238513 0.00920296 0.01020408 0.00955772 0.00960541 0.00908184 0.00864434 0.00876403 0.00919509] mean value: 0.009907960891723633 key: score_time value: [0.01181912 0.01171041 0.00913239 0.00935698 0.00874352 0.00845289 0.00830865 0.00832915 0.00893331 0.00853944] mean value: 0.009332585334777831 key: test_mcc value: [ 0. 0. 0. 0. -0.09652342 -0.13900961 -0.13900961 0. -0.09652342 0. ] mean value: -0.04710660543691024 key: train_mcc value: [0.18231228 0.21036008 0.21426967 0.16885707 0.07233932 0.17191815 0.2871918 0.19289384 0.17839573 0.17191815] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) mean value: 0.18504561006487819 key: test_fscore value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_fscore value: [0.14035088 0.14035088 0.16949153 0.16393443 0.07142857 0.16666667 0.28125 0.16949153 0.0754717 0.16666667] mean value: 0.15451028343380432 key: test_precision value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_precision value: [0.66666667 0.8 0.71428571 0.55555556 0.4 0.55555556 0.69230769 0.625 1. 0.55555556] mean value: 0.656492673992674 key: test_recall value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_recall value: [0.07843137 0.07692308 0.09615385 0.09615385 0.03921569 0.09803922 0.17647059 0.09803922 0.03921569 0.09803922] mean value: 0.0896681749622926 key: test_accuracy value: [0.8 0.82758621 0.82758621 0.82758621 0.75862069 0.72413793 0.72413793 0.79310345 0.75862069 0.79310345] mean value: 0.783448275862069 key: train_accuracy value: [0.81226054 0.8129771 0.8129771 0.80534351 0.80152672 0.80916031 0.82442748 0.8129771 0.8129771 0.80916031] mean value: 0.8113787253955719 key: test_roc_auc value: [0.5 0.5 0.5 0.5 0.47826087 0.45652174 0.45652174 0.5 0.47826087 0.5 ] mean value: 0.48695652173913045 key: train_roc_auc value: [0.53445378 0.53608059 0.54331502 0.53855311 0.51249884 0.53954093 0.57875662 0.5419106 0.51960784 0.53954093] mean value: 0.5384258274946874 key: test_jcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_jcc value: [0.0754717 0.0754717 0.09259259 0.08928571 0.03703704 0.09090909 0.16363636 0.09259259 0.03921569 0.09090909] mean value: 0.08471215644634068 MCC on Blind test: 0.41 MCC on Training: -0.05 Running classifier: 15 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.00929117 0.0103488 0.01015997 0.01019001 0.01020026 0.01032948 0.00965953 0.01005673 0.01058269 0.0100441 ] mean value: 0.010086274147033692 key: score_time value: [0.00842071 0.00941586 0.00916457 0.00957227 0.00940919 0.00934172 0.00867248 0.00902224 0.00942707 0.0093689 ] mean value: 0.009181499481201172 key: test_mcc value: [-0.19611614 -0.15504342 0.2360294 -0.124226 -0.09652342 0.38554329 -0.13900961 0.10975393 0.38554329 -0.09652342] mean value: 0.030942790617947892 key: train_mcc value: [0.23360986 0.22888719 0.22984174 0.25142865 0.2217027 0.25228728 0.23349059 0.2012544 0.26118423 0.22220451] mean value: 0.23358911626137827 key: test_fscore value: [0. 0. 0.28571429 0. 0. 0.44444444 0. 0.30769231 0.44444444 0. ] mean value: 0.14822954822954823 key: train_fscore value: [0.31578947 0.2972973 0.28169014 0.32 0.28169014 0.27272727 0.30136986 0.28947368 0.25396825 0.2972973 ] mean value: 0.29113034238886976 key: test_precision value: [0. 0. 0.5 0. 0. 0.66666667 0. 0.28571429 0.66666667 0. ] mean value: 0.2119047619047619 key: train_precision value: [0.48 0.5 0.52631579 0.52173913 0.5 0.6 0.5 0.44 0.66666667 0.47826087] mean value: 0.5212982456140351 key: test_recall value: [0. 0. 0.2 0. 0. 0.33333333 0. 0.33333333 0.33333333 0. ] mean value: 0.12 key: train_recall value: [0.23529412 0.21153846 0.19230769 0.23076923 0.19607843 0.17647059 0.21568627 0.21568627 0.15686275 0.21568627] mean value: 0.20463800904977378 key: test_accuracy value: [0.66666667 0.72413793 0.82758621 0.75862069 0.75862069 0.82758621 0.72413793 0.68965517 0.82758621 0.75862069] mean value: 0.7563218390804598 key: train_accuracy value: [0.80076628 0.80152672 0.80534351 0.80534351 0.80534351 0.81679389 0.80534351 0.79389313 0.82061069 0.80152672] mean value: 0.80564914743646 key: test_roc_auc value: [0.41666667 0.4375 0.57916667 0.45833333 0.47826087 0.64492754 0.45652174 0.55797101 0.64492754 0.47826087] mean value: 0.5152536231884058 key: train_roc_auc value: [0.58669468 0.57957875 0.57472527 0.58919414 0.57434253 0.57401728 0.58177679 0.57466778 0.5689527 0.57940712] mean value: 0.5783357050439571 key: test_jcc value: [0. 0. 0.16666667 0. 0. 0.28571429 0. 0.18181818 0.28571429 0. ] mean value: 0.09199134199134198 key: train_jcc value: [0.1875 0.17460317 0.16393443 0.19047619 0.16393443 0.15789474 0.17741935 0.16923077 0.14545455 0.17460317] mean value: 0.1705050798507686 MCC on Blind test: 0.0 MCC on Training: 0.03 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.01006579 0.012743 0.01404023 0.01419902 0.01640463 0.01475835 0.0162847 0.01680541 0.01670885 0.01459789] mean value: 0.014660787582397462 key: score_time value: [0.00819111 0.00999808 0.01210189 0.01225567 0.01227212 0.01200533 0.01214099 0.01209283 0.01202941 0.01201129] mean value: 0.011509871482849121 key: test_mcc value: [-0.13363062 0.33101875 0. 0. 0. 0.08917507 -0.31524416 -0.1060244 0.2892177 0.19693028] mean value: 0.03514426108129105 key: train_mcc value: [0.32946284 0.59574164 0. 0.21628118 0.25326564 0.30790687 0.61794188 0.16318286 0.40810907 0.51223976] mean value: 0.34041317372260976 key: test_fscore value: [0. 0.46153846 0. 0. 0. 0.35714286 0. 0.3125 0.45454545 0.25 ] mean value: 0.18357267732267732 key: train_fscore value: [0.29032258 0.67326733 0. 0.10909091 0.14545455 0.44776119 0.69565217 0.3554007 0.50793651 0.53333333] mean value: 0.3758219268000136 key: test_precision value: [0. 0.375 0. 0. 0. 0.22727273 0. 0.19230769 0.3125 0.5 ] mean value: 0.16070804195804195 key: train_precision value: [0.81818182 0.69387755 0. 1. 1. 0.3 0.625 0.21610169 0.34782609 0.83333333] mean value: 0.5834320484407335 key: test_recall value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) [0. 0.6 0. 0. 0. 0.83333333 0. 0.83333333 0.83333333 0.16666667] mean value: 0.32666666666666666 key: train_recall value: [0.17647059 0.65384615 0. 0.05769231 0.07843137 0.88235294 0.78431373 1. 0.94117647 0.39215686] mean value: 0.49664404223227754 key: test_accuracy value: [0.73333333 0.75862069 0.82758621 0.82758621 0.79310345 0.37931034 0.51724138 0.24137931 0.5862069 0.79310345] mean value: 0.6457471264367817 key: train_accuracy value: [0.83141762 0.8740458 0.80152672 0.8129771 0.82061069 0.57633588 0.86641221 0.29389313 0.64503817 0.86641221] mean value: 0.738866953291802 key: test_roc_auc value: [0.45833333 0.69583333 0.5 0.5 0.5 0.54710145 0.32608696 0.46014493 0.67753623 0.5615942 ] mean value: 0.5226630434782609 key: train_roc_auc value: [0.58347339 0.79120879 0.5 0.52884615 0.53921569 0.6923613 0.83528482 0.56161137 0.75731809 0.68659976] mean value: 0.6475919376135434 key: test_jcc value: [0. 0.3 0. 0. 0. 0.2173913 0. 0.18518519 0.29411765 0.14285714] mean value: 0.11395512794489777 key: train_jcc value: [0.16981132 0.50746269 0. 0.05769231 0.07843137 0.28846154 0.53333333 0.21610169 0.34042553 0.36363636] mean value: 0.2555356149824592 MCC on Blind test: -0.15 MCC on Training: 0.04 Running classifier: 17 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.01846361 0.02222204 0.02209902 0.02280188 0.02223444 0.02264404 0.02307224 0.0225637 0.02322006 0.02148795] mean value: 0.02208089828491211 key: score_time value: [0.0121901 0.0122118 0.01230264 0.01243663 0.01258826 0.01227212 0.01266503 0.01219821 0.01235247 0.01219606] mean value: 0.01234133243560791 key: test_mcc value: [ 0.04902903 -0.18257419 -0.124226 -0.15504342 0. 0.19693028 0. 0.1060244 0.19693028 0.1060244 ] mean value: 0.019309479923619886 key: train_mcc value: [0.25315586 0.35664498 0.35664498 0.30765475 0.28370996 0.28370996 0.28370996 0.31139511 0.25326564 0.25326564] mean value: 0.2943156833022445 key: test_fscore value: [0.2 0. 0. 0. 0. 0.25 0. 0.22222222 0.25 0.22222222] mean value: 0.11444444444444443 key: train_fscore value: [0.14545455 0.26666667 0.26666667 0.20689655 0.17857143 0.17857143 0.17857143 0.21052632 0.14545455 0.14545455] mean value: 0.19228341229248672 key: test_precision value: [0.25 0. 0. 0. 0. 0.5 0. 0.33333333 0.5 0.33333333] mean value: 0.19166666666666665 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.16666667 0. 0. 0. 0. 0.16666667 0. 0.16666667 0.16666667 0.16666667] mean value: 0.08333333333333333 key: train_recall value: [0.07843137 0.15384615 0.15384615 0.11538462 0.09803922 0.09803922 0.09803922 0.11764706 0.07843137 0.07843137] mean value: 0.10701357466063348 key: test_accuracy value: [0.73333333 0.68965517 0.75862069 0.72413793 0.79310345 0.79310345 0.79310345 0.75862069 0.79310345 0.75862069] mean value: 0.7595402298850575 key: train_accuracy value: [0.81992337 0.83206107 0.83206107 0.82442748 0.82442748 0.82442748 0.82442748 0.82824427 0.82061069 0.82061069] mean value: 0.8251221081571174 key: test_roc_auc value: [0.52083333 0.41666667 0.45833333 0.4375 0.5 0.5615942 0.5 0.53985507 0.5615942 0.53985507] mean value: 0.5036231884057971 key: train_roc_auc value: [0.53921569 0.57692308 0.57692308 0.55769231 0.54901961 0.54901961 0.54901961 0.55882353 0.53921569 0.53921569] mean value: 0.5535067873303168 key: test_jcc value: [0.11111111 0. 0. 0. 0. 0.14285714 0. 0.125 0.14285714 0.125 ] mean value: 0.06468253968253967 key: train_jcc value: [0.07843137 0.15384615 0.15384615 0.11538462 0.09803922 0.09803922 0.09803922 0.11764706 0.07843137 0.07843137] mean value: 0.10701357466063348 MCC on Blind test: 0.12 MCC on Training: 0.02 Running classifier: 18 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42))]) key: fit_time value: [0.62879348 0.60427117 0.63881326 0.59716582 0.70174646 0.65222359 0.64095283 0.61650419 0.63356233 0.62709975] mean value: 0.6341132879257202 key: score_time value: [0.16397905 0.16454244 0.16855454 0.16428733 0.1610322 0.18599153 0.15581226 0.17914867 0.18749714 0.17951536] mean value: 0.1710360527038574 key: test_mcc value: [ 0. -0.15504342 -0.08625819 0. 0. -0.09652342 -0.13900961 -0.09652342 -0.09652342 -0.13900961] mean value: -0.08088910853365752 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8 0.72413793 0.79310345 0.82758621 0.79310345 0.75862069 0.72413793 0.75862069 0.75862069 0.72413793] mean value: 0.7662068965517241 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.5 0.4375 0.47916667 0.5 0.5 0.47826087 0.45652174 0.47826087 0.47826087 0.45652174] mean value: 0.47644927536231885 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.15 MCC on Training: -0.08 Running classifier: 19 Model_name: Random Forest2 Model func: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea...age_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42))]) key: fit_time value: [0.93180728 1.00130224 0.9421227 0.92246556 0.92105865 0.94207573 0.94890165 0.97795987 0.9437573 1.02974367] mean value: 0.9561194658279419 key: score_time value: [0.15065503 0.20829916 0.24284887 0.2102387 0.18846917 0.22759247 0.22809911 0.20838118 0.21353674 0.19064283] mean value: 0.2068763256072998 key: test_mcc value: [ 0. 0. -0.08625819 0. 0. -0.09652342 0. 0. 0. 0. ] mean value: -0.018278161273096234 key: train_mcc value: [0.56858275 0.54581984 0.51250772 0.56192925 0.55245573 0.5143453 0.49665845 0.60042292 0.46475936 0.4456324 ] mean value: 0.5263113713424737 key: test_fscore value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_fscore value: [0.54285714 0.51428571 0.47058824 0.53521127 0.52173913 0.49275362 0.47058824 0.58333333 0.40625 0.38095238] mean value: 0.49185590632456294 key: test_precision value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_precision value: [1. 1. 1. 1. 1. 0.94444444 0.94117647 1. 1. 1. ] mean value: 0.988562091503268 key: test_recall value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_recall value: [0.37254902 0.34615385 0.30769231 0.36538462 0.35294118 0.33333333 0.31372549 0.41176471 0.25490196 0.23529412] mean value: 0.3293740573152338 key: test_accuracy value: [0.8 0.82758621 0.79310345 0.82758621 0.79310345 0.75862069 0.79310345 0.79310345 0.79310345 0.79310345] mean value: 0.7972413793103449 key: train_accuracy value: [0.87739464 0.87022901 0.86259542 0.8740458 0.8740458 0.86641221 0.86259542 0.88549618 0.85496183 0.85114504] mean value: 0.8678921353572578 key: test_roc_auc value: [0.5 0.5 0.47916667 0.5 0.5 0.47826087 0.5 0.5 0.5 0.5 ] mean value: 0.49574275362318837 key: train_roc_auc value: [0.68627451 0.67307692 0.65384615 0.68269231 0.67647059 0.664297 0.65449308 0.70588235 0.62745098 0.61764706] mean value: 0.6642130950083278 key: test_jcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_jcc value: [0.37254902 0.34615385 0.30769231 0.36538462 0.35294118 0.32692308 0.30769231 0.41176471 0.25490196 0.23529412] mean value: 0.32812971342383107 MCC on Blind test: 0.0 MCC on Training: -0.02 Running classifier: 20 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.03831434 0.03500009 0.03519893 0.04107475 0.02628541 0.01437926 0.01457024 0.02631927 0.03640628 0.03953385] mean value: 0.03070824146270752 key: score_time value: [0.02158308 0.02237678 0.02278018 0.02247024 0.01215816 0.01216722 0.01214576 0.03273249 0.02781367 0.02796769] mean value: 0.021419525146484375 key: test_mcc value: [-0.13363062 0.03333333 0.2360294 -0.124226 0.37000643 0.21758446 -0.13900961 -0.09652342 0.28942722 0.1060244 ] mean value: 0.07590155989258954 key: train_mcc value: [0.59672472 0.53942574 0.6049796 0.55577606 0.49665845 0.47882173 0.57907583 0.54843706 0.47848106 0.58107318] mean value: 0.5459453427711142 key: test_fscore value: [0. 0.2 0.28571429 0. 0.28571429 0.36363636 0. 0. 0.4 0.22222222] mean value: 0.17572871572871573 key: train_fscore value: [0.59459459 0.54054054 0.60526316 0.56 0.47058824 0.50666667 0.58666667 0.53521127 0.44776119 0.57534247] mean value: 0.5422634789046231 key: test_precision value: [0. 0.2 0.5 0. 1. 0.4 0. 0. 0.5 0.33333333] mean value: 0.29333333333333333 key: train_precision value: [0.95652174 0.90909091 0.95833333 0.91304348 0.94117647 0.79166667 0.91666667 0.95 0.9375 0.95454545] mean value: 0.9228544718282571 key: test_recall value: [0. 0.2 0.2 0. 0.16666667 0.33333333 0. 0. 0.33333333 0.16666667] mean value: 0.14 key: train_recall value: [0.43137255 0.38461538 0.44230769 0.40384615 0.31372549 0.37254902 0.43137255 0.37254902 0.29411765 0.41176471] mean value: 0.3858220211161387 key: test_accuracy value: [0.73333333 0.72413793 0.82758621 0.75862069 0.82758621 0.75862069 0.72413793 0.75862069 0.79310345 0.75862069] mean value: 0.7664367816091955 key: train_accuracy value: [0.88505747 0.87022901 0.88549618 0.8740458 0.86259542 0.85877863 0.88167939 0.8740458 0.85877863 0.88167939] mean value: 0.8732385715539177 key: test_roc_auc value: [0.45833333 0.51666667 0.57916667 0.45833333 0.58333333 0.60144928 0.45652174 0.47826087 0.62318841 0.53985507] mean value: 0.5295108695652174 key: train_roc_auc value: [0.71330532 0.68754579 0.71877289 0.69716117 0.65449308 0.67442617 0.71094694 0.68390484 0.64468916 0.70351268] mean value: 0.688875804058408 key: test_jcc value: [0. 0.11111111 0.16666667 0. 0.16666667 0.22222222 0. 0. 0.25 0.125 ] mean value: 0.10416666666666666 key: train_jcc value: [0.42307692 0.37037037 0.43396226 0.38888889 0.30769231 0.33928571 0.41509434 0.36538462 0.28846154 0.40384615] mean value: 0.37360631157800966 MCC on Blind test: 0.12 MCC on Training: 0.08 Running classifier: 21 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=3) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=3))]) key: fit_time value: [0.0661304 0.05325651 0.06285739 0.17912173 0.15246034 0.1375072 0.11358428 0.06274962 0.12163186 0.09195018] mean value: 0.10412495136260987 key: score_time value: [0.01314521 0.01459146 0.03206563 0.03299379 0.02993536 0.02863908 0.01442933 0.02934027 0.02843904 0.01407838] mean value: 0.023765754699707032 key: test_mcc value: [ 0. -0.08625819 -0.08625819 -0.08625819 0. 0. 0. 0. 0.19693028 0. ] mean value: -0.006184430481059 key: train_mcc value: [0.32946284 0.30102534 0.33295605 0.32648375 0.30495191 0.27777609 0.35340832 0.27722402 0.17464527 0.33070507] mean value: 0.3008638642885503 key: test_fscore value: [0. 0. 0. 0. 0. 0. 0. 0. 0.25 0. ] mean value: 0.025 key: train_fscore value: [0.29032258 0.23333333 0.23728814 0.26229508 0.23728814 0.23333333 0.31746032 0.20689655 0.10909091 0.26666667] mean value: 0.23939750454075132 key: test_precision value: [0. 0. 0. 0. 0. 0. 0. 0. 0.5 0. ] mean value: 0.05 key: train_precision value: [0.81818182 0.875 1. 0.88888889 0.875 0.77777778 0.83333333 0.85714286 0.75 0.88888889] mean value: 0.8564213564213565 key: test_recall value: [0. 0. 0. 0. 0. 0. 0. 0. 0.16666667 0. ] mean value: 0.016666666666666666 key: train_recall value: [0.17647059 0.13461538 0.13461538 0.15384615 0.1372549 0.1372549 0.19607843 0.11764706 0.05882353 0.15686275] mean value: 0.14034690799396682 key: test_accuracy value: [0.8 0.79310345 0.79310345 0.79310345 0.79310345 0.79310345 0.79310345 0.79310345 0.79310345 0.79310345] mean value: 0.7937931034482759 key: train_accuracy value: [0.83141762 0.82442748 0.82824427 0.82824427 0.82824427 0.82442748 0.83587786 0.82442748 0.8129771 0.83206107] mean value: 0.8270348922230996 key: test_roc_auc value: [0.5 0.47916667 0.47916667 0.47916667 0.5 0.5 0.5 0.5 0.5615942 0.5 ] mean value: 0.4999094202898551 key: train_roc_auc value: [0.58347339 0.56492674 0.56730769 0.57454212 0.56625778 0.56388811 0.59329988 0.55645386 0.5270421 0.5760617 ] mean value: 0.567325338447446 key: test_jcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0.14285714 0. ] mean value: 0.014285714285714285 key: train_jcc value: [0.16981132 0.13207547 0.13461538 0.1509434 0.13461538 0.13207547 0.18867925 0.11538462 0.05769231 0.15384615] mean value: 0.13697387518142237 MCC on Blind test: -0.11 MCC on Training: -0.01 Running classifier: 22 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.03405571 0.01225781 0.01169968 0.0114491 0.01146841 0.0115006 0.01136088 0.01155925 0.01164985 0.01160741] mean value: 0.013860869407653808 key: score_time value: [0.00979877 0.00950503 0.00915623 0.00912094 0.00942802 0.00915146 0.00912428 0.00923467 0.00924253 0.00925303] mean value: 0.009301495552062989 key: test_mcc value: [ 0. -0.08625819 0. 0. 0. 0. 0. 0. 0. 0. ] mean value: -0.008625819491779428 key: train_mcc value: [0. 0.17625291 0. 0. 0. 0. 0.12590294 0. 0. 0. ] mean value: 0.0302155855505856 key: test_fscore value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_fscore value: [0. 0.07407407 0. 0. 0. 0. 0.03846154 0. 0. 0. ] mean value: 0.011253561253561254 key: test_precision value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_precision value: [0. 1. 0. 0. 0. 0. 1. 0. 0. 0.] mean value: 0.2 key: test_recall value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_recall value: [0. 0.03846154 0. 0. 0. 0. 0.01960784 0. 0. 0. ] mean value: 0.0058069381598793365 key: test_accuracy value: [0.8 0.79310345 0.82758621 0.82758621 0.79310345 0.79310345 0.79310345 0.79310345 0.79310345 0.79310345] mean value: 0.8006896551724138 key: train_accuracy value: [0.8045977 0.80916031 0.80152672 0.80152672 0.80534351 0.80534351 0.80916031 0.80534351 0.80534351 0.80534351] mean value: 0.8052689304202859 key: test_roc_auc value: [0.5 0.47916667 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 ] mean value: 0.4979166666666667 key: train_roc_auc value: [0.5 0.51923077 0.5 0.5 0.5 0.5 0.50980392 0.5 0.5 0.5 ] mean value: 0.5029034690799397 key: test_jcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_jcc value: [0. 0.03846154 0. 0. 0. 0. 0.01960784 0. 0. 0. ] mean value: 0.0058069381598793365 MCC on Blind test: 0.0 MCC on Training: -0.01 Running classifier: 23 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.01118541 0.01706719 0.01672292 0.01713657 0.01478791 0.01619339 0.01668549 0.01612592 0.01904655 0.01640463] mean value: 0.016135597229003908 key: score_time value: /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:463: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_CV['source_data'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:490: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_BT['source_data'] = 'BT' [0.00840235 0.0111711 0.01133895 0.01150346 0.01161051 0.01135588 0.01147246 0.01197386 0.01172662 0.01187015] mean value: 0.011242532730102539 key: test_mcc value: [ 0.11111111 0.10708993 -0.08625819 0. 0. 0.21758446 -0.13900961 0. 0.30868293 0.37000643] mean value: 0.0889207058390655 key: train_mcc value: [0.42017367 0.45229022 0.45917684 0.41510932 0.37774335 0.45841362 0.51391524 0.17839573 0.58359222 0.28370996] mean value: 0.4142520185953177 key: test_fscore value: [0.22222222 0.31578947 0. 0. 0. 0.36363636 0. 0. 0.46153846 0.28571429] mean value: 0.16489008067955435 key: train_fscore value: [0.375 0.54857143 0.4 0.36923077 0.32258065 0.47222222 0.54545455 0.0754717 0.66666667 0.17857143] mean value: 0.39537694039915583 key: test_precision value: [0.33333333 0.21428571 0. 0. 0. 0.4 0. 0. 0.42857143 1. ] mean value: 0.23761904761904762 key: train_precision value: [0.92307692 0.3902439 1. 0.92307692 0.90909091 0.80952381 0.80769231 1. 0.64814815 1. ] mean value: 0.8410852923048043 key: test_recall value: [0.16666667 0.6 0. 0. 0. 0.33333333 0. 0. 0.5 0.16666667] mean value: 0.17666666666666667 key: train_recall value: [0.23529412 0.92307692 0.25 0.23076923 0.19607843 0.33333333 0.41176471 0.03921569 0.68627451 0.09803922] mean value: 0.3403846153846154 key: test_accuracy value: [0.76666667 0.55172414 0.79310345 0.82758621 0.79310345 0.75862069 0.72413793 0.79310345 0.75862069 0.82758621] mean value: 0.7594252873563219 key: train_accuracy value: [0.8467433 0.69847328 0.85114504 0.84351145 0.83969466 0.85496183 0.86641221 0.8129771 0.86641221 0.82442748] mean value: 0.830475856219473 key: test_roc_auc value: [0.54166667 0.57083333 0.47916667 0.5 0.5 0.60144928 0.45652174 0.5 0.66304348 0.58333333] mean value: 0.5396014492753622 key: train_roc_auc value: [0.61526611 0.78296703 0.625 0.61300366 0.59566955 0.65718799 0.69403401 0.51960784 0.79811356 0.54901961] mean value: 0.6449869364442824 key: test_jcc value: [0.125 0.1875 0. 0. 0. 0.22222222 0. 0. 0.3 0.16666667] mean value: 0.10013888888888889 key: train_jcc value: [0.23076923 0.37795276 0.25 0.22641509 0.19230769 0.30909091 0.375 0.03921569 0.5 0.09803922] mean value: 0.2598790584373751 MCC on Blind test: -0.11 MCC on Training: 0.09 Running classifier: 24 Model_name: XGBoost Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.0789206 0.08110189 0.08062935 0.08371949 0.0839684 0.08368826 0.07559204 0.07441282 0.07837677 0.07777214] mean value: 0.07981817722320557 key: score_time value: [0.0113399 0.01147795 0.01065087 0.01080871 0.01072431 0.01180291 0.01074934 0.01162863 0.01077986 0.01097393] mean value: 0.011093640327453613 key: test_mcc value: [ 0.37139068 0.2360294 -0.08625819 0.14470719 0.19693028 0.38554329 0.21758446 0.1060244 0.1060244 0.19693028] mean value: 0.18749061809221376 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.28571429 0.28571429 0. 0.25 0.25 0.44444444 0.36363636 0.22222222 0.22222222 0.25 ] mean value: 0.2573953823953824 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.5 0. 0.33333333 0.5 0.66666667 0.4 0.33333333 0.33333333 0.5 ] mean value: 0.45666666666666667 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.16666667 0.2 0. 0.2 0.16666667 0.33333333 0.33333333 0.16666667 0.16666667 0.16666667] mean value: 0.19 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.83333333 0.82758621 0.79310345 0.79310345 0.79310345 0.82758621 0.75862069 0.75862069 0.75862069 0.79310345] mean value: 0.7936781609195402 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.58333333 0.57916667 0.47916667 0.55833333 0.5615942 0.64492754 0.60144928 0.53985507 0.53985507 0.5615942 ] mean value: 0.5649275362318841 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.16666667 0.16666667 0. 0.14285714 0.14285714 0.28571429 0.22222222 0.125 0.125 0.14285714] mean value: 0.15198412698412697 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.12 MCC on Training: 0.19 Extracting tts_split_name: sl Total cols in each df: CV df: 8 metaDF: 17 Adding column: Model_name Total cols in bts df: BT_df: 8 First proceeding to rowbind CV and BT dfs: Final output should have: 25 columns Combinig 2 using pd.concat by row ~ rowbind Checking Dims of df to combine: Dim of CV: (24, 8) Dim of BT: (24, 8) 8 Number of Common columns: 8 These are: ['source_data', 'Precision', 'JCC', 'MCC', 'Recall', 'Accuracy', 'F1', 'ROC_AUC'] Concatenating dfs with different resampling methods [WF]: Split type: sl No. of dfs combining: 2 PASS: 2 dfs successfully combined nrows in combined_df_wf: 48 ncols in combined_df_wf: 8 PASS: proceeding to merge metadata with CV and BT dfs Adding column: Model_name ========================================================= SUCCESS: Ran multiple classifiers ======================================================= ============================================================== Running several classification models (n): 24 List of models: ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... 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Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... ubN†”N}”t”R”sbŒargs”)Œkwargs”}”Œ loky_pickler”Œ cloudpickle”u[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.4s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.4s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished ('Gaussian NB', GaussianNB()) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('K-Nearest Neighbors', KNeighborsClassifier()) ('LDA', LinearDiscriminantAnalysis()) ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(cv=3, random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Multinomial', MultinomialNB()) ('Naive Bayes', BernoulliNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=12, random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Random Forest', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42)) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=3)) ('SVC', SVC(random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=12, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ================================================================ Running classifier: 1 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.21172881 0.21087551 0.21223283 0.21504736 0.21554327 0.21233106 0.21231866 0.21051574 0.2120347 0.21276402] mean value: 0.2125391960144043 key: score_time value: [0.0150888 0.01516891 0.01574087 0.01646376 0.01519895 0.01765251 0.01510668 0.01500845 0.01524329 0.01516724] mean value: 0.015583944320678712 key: test_mcc value: [0.63294907 0.62966842 0.75645593 0.32602701 0.66243303 0.36231884 0.44646172 0.7023605 0.78334945 0.69631062] mean value: 0.59983345962771 key: train_mcc value: [0.94775328 0.95249379 0.93825322 0.971673 0.96199503 0.9572557 0.92403349 0.96217047 0.96208531 0.95735672] mean value: 0.9535070008277573 key: test_fscore value: [0.82352941 0.7804878 0.88 0.68 0.82608696 0.68085106 0.73469388 0.85714286 0.89361702 0.85106383] mean value: 0.8007472822751988 key: train_fscore value: [0.97399527 0.97630332 0.96912114 0.98564593 0.98095238 0.97862233 0.96208531 0.98113208 0.98104265 0.9787234 ] mean value: 0.9767623813115709 key: test_precision value: [0.75 0.88888889 0.81481481 0.62962963 0.86363636 0.69565217 0.72 0.84 0.875 0.83333333] mean value: 0.7910955204216074 key: train_precision value: [0.97169811 0.97630332 0.97142857 0.99516908 0.98095238 0.97630332 0.95754717 0.97196262 0.98104265 0.97641509] mean value: 0.9758822317787004 key: test_recall value: [0.91304348 0.69565217 0.95652174 0.73913043 0.79166667 0.66666667 0.75 0.875 0.91304348 0.86956522] mean value: 0.8170289855072463 key: train_recall value: [0.97630332 0.97630332 0.96682464 0.97630332 0.98095238 0.98095238 0.96666667 0.99047619 0.98104265 0.98104265] mean value: 0.9776867524260888 key: test_accuracy value: [0.80851064 0.80851064 0.87234043 0.65957447 0.82978723 0.68085106 0.72340426 0.85106383 0.89130435 0.84782609] mean value: 0.7973172987974098 key: train_accuracy value: [0.97387173 0.97624703 0.96912114 0.98574822 0.98099762 0.97862233 0.96199525 0.98099762 0.98104265 0.97867299] mean value: 0.9767316589929191 key: test_roc_auc value: [0.81068841 0.80615942 0.8740942 0.66123188 0.83061594 0.68115942 0.72282609 0.85054348 0.89130435 0.84782609] mean value: 0.7976449275362317 key: train_roc_auc value: [0.97386594 0.9762469 0.96912661 0.98577071 0.98099752 0.97862785 0.96200632 0.98102009 0.98104265 0.97867299] mean value: 0.97673775671406 key: test_jcc value: [0.7 0.64 0.78571429 0.51515152 0.7037037 0.51612903 0.58064516 0.75 0.80769231 0.74074074] mean value: 0.673977674655094 key: train_jcc value: [0.94930876 0.9537037 0.94009217 0.97169811 0.96261682 0.95813953 0.92694064 0.96296296 0.9627907 0.95833333] mean value: 0.9546586729123986 MCC on Blind test: -0.1 MCC on Training: 0.6 Running classifier: 2 Model_name: Bagging Classifier Model func: BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3))]) key: fit_time value: [0.2375443 0.25949955 0.22673535 0.2656548 0.25132394 0.23817396 0.28853154 0.26925492 0.3055656 0.26827693] mean value: 0.26105608940124514 key: score_time value: [0.0566566 0.04149699 0.04349065 0.0658257 0.03768516 0.07169724 0.06948304 0.05089331 0.06004429 0.05975652] mean value: 0.055702948570251466 key: test_mcc value: [0.87979456 0.87917396 0.91485507 0.7085716 0.87318841 0.75645593 0.65942029 0.82971014 0.87038828 0.87038828] mean value: 0.8241946524830202 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.93877551 0.93023256 0.95652174 0.85714286 0.93617021 0.86363636 0.83333333 0.91666667 0.93617021 0.93333333] mean value: 0.9101982787118519 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.88461538 1. 0.95652174 0.80769231 0.95652174 0.95 0.83333333 0.91666667 0.91666667 0.95454545] mean value: 0.9176563291780683 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 0.86956522 0.95652174 0.91304348 0.91666667 0.79166667 0.83333333 0.91666667 0.95652174 0.91304348] mean value: 0.9067028985507246 key: [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.1s remaining: 2.3s Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.2s remaining: 2.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.3s remaining: 0.4s Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.3s remaining: 0.4s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.3s remaining: 2.6s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.4s finished Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.4s remaining: 2.8s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.4s remaining: 0.5s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.4s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.4s remaining: 2.8s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.4s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.4s remaining: 2.8s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.4s remaining: 0.5s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.4s remaining: 2.8s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.4s remaining: 0.5s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.4s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.4s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.4s remaining: 2.8s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.5s remaining: 2.9s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.5s remaining: 2.9s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.5s remaining: 0.5s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.5s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.5s remaining: 0.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.5s remaining: 0.5s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.5s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.5s remaining: 0.5s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.5s remaining: 0.5s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.5s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.5s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.5s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.93617021 0.93617021 0.95744681 0.85106383 0.93617021 0.87234043 0.82978723 0.91489362 0.93478261 0.93478261] mean value: 0.9103607770582794 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.9375 0.93478261 0.95742754 0.85235507 0.9365942 0.8740942 0.82971014 0.91485507 0.93478261 0.93478261] mean value: 0.9106884057971014 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.88461538 0.86956522 0.91666667 0.75 0.88 0.76 0.71428571 0.84615385 0.88 0.875 ] mean value: 0.8376286829112918 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.12 MCC on Training: 0.82 Running classifier: 3 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.02687049 0.02407742 0.02627039 0.03076768 0.0254209 0.02507114 0.02380562 0.02561712 0.03274417 0.02586579] mean value: 0.02665107250213623 key: score_time value: [0.00884366 0.00945067 0.00872493 0.00883007 0.00886345 0.00912428 0.00898147 0.00887418 0.00921845 0.00914407] mean value: 0.009005522727966309 key: test_mcc value: [0.79418308 0.7023605 0.62296012 0.49819858 0.62091661 0.53734864 0.45948781 0.58127976 0.69631062 0.75056834] mean value: 0.6263614060365161 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.89795918 0.84444444 0.81632653 0.76 0.82352941 0.75555556 0.69767442 0.80769231 0.85106383 0.88 ] mean value: 0.8134245682134615 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.84615385 0.86363636 0.76923077 0.7037037 0.77777778 0.80952381 0.78947368 0.75 0.83333333 0.81481481] mean value: 0.7957648102384944 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.95652174 0.82608696 0.86956522 0.82608696 0.875 0.70833333 0.625 0.875 0.86956522 0.95652174] mean value: 0.838768115942029 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.89361702 0.85106383 0.80851064 0.74468085 0.80851064 0.76595745 0.72340426 0.78723404 0.84782609 0.86956522] mean value: 0.8100370027752082 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.89492754 0.85054348 0.80978261 0.74637681 0.80706522 0.76721014 0.72554348 0.78532609 0.84782609 0.86956522] mean value: 0.8104166666666668 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.81481481 0.73076923 0.68965517 0.61290323 0.7 0.60714286 0.53571429 0.67741935 0.74074074 0.78571429] mean value: 0.6894873967955168 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.49 MCC on Training: 0.63 Running classifier: 4 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.009974 0.00999379 0.0098002 0.00956535 0.00967455 0.00964141 0.00989151 0.00974131 0.00978327 0.00981355] mean value: 0.009787893295288086 key: score_time value: [0.00885653 0.00848246 0.0086782 0.00849223 0.00857186 0.00851893 0.008569 0.00879216 0.00850773 0.00850296] mean value: 0.008597207069396973 key: test_mcc value: [0.42102089 0.53176131 0.53734864 0.44646172 0.4121128 0.32123465 0.57713344 0.57427536 0.78334945 0.52223297] mean value: 0.5126931226018667 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.73076923 0.75555556 0.7755102 0.71111111 0.68181818 0.65217391 0.7826087 0.79166667 0.89361702 0.76595745] mean value: 0.7540788026783136 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.65517241 0.77272727 0.73076923 0.72727273 0.75 0.68181818 0.81818182 0.79166667 0.875 0.75 ] mean value: 0.7552608311229001 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.82608696 0.73913043 0.82608696 0.69565217 0.625 0.625 0.75 0.79166667 0.91304348 0.7826087 ] mean value: 0.757427536231884 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.70212766 0.76595745 0.76595745 0.72340426 0.70212766 0.65957447 0.78723404 0.78723404 0.89130435 0.76086957] mean value: 0.7545790934320074 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.70471014 0.76539855 0.76721014 0.72282609 0.70380435 0.66032609 0.78804348 0.78713768 0.89130435 0.76086957] mean value: 0.7551630434782609 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.57575758 0.60714286 0.63333333 0.55172414 0.51724138 0.48387097 0.64285714 0.65517241 0.80769231 0.62068966] mean value: 0.6095481770732049 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.24 MCC on Training: 0.51 Running classifier: 5 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.12470484 0.12798667 0.12881851 0.12675786 0.13381267 0.13320899 0.12927175 0.12853575 0.13246989 0.12586284] mean value: 0.12914297580718995 key: score_time value: [0.01925802 0.0194335 0.01787972 0.01833653 0.01900029 0.02086759 0.01935363 0.01892853 0.01953816 0.0186491 ] mean value: 0.019124507904052734 key: test_mcc value: [0.95825929 0.78804348 0.7876601 0.61706091 0.83303222 0.61706091 0.70289855 0.66801039 0.87705802 0.87038828] mean value: 0.7719472145577224 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.97777778 0.89361702 0.88888889 0.8 0.91304348 0.81632653 0.85106383 0.84615385 0.93877551 0.93333333] mean value: 0.8858980216294873 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.875 0.90909091 0.81818182 0.95454545 0.8 0.86956522 0.78571429 0.88461538 0.95454545] mean value: 0.8851258524084612 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.95652174 0.91304348 0.86956522 0.7826087 0.875 0.83333333 0.83333333 0.91666667 1. 0.91304348] mean value: 0.8893115942028984 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.9787234 0.89361702 0.89361702 0.80851064 0.91489362 0.80851064 0.85106383 0.82978723 0.93478261 0.93478261] mean value: 0.8848288621646624 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.97826087 0.89402174 0.89311594 0.80797101 0.91576087 0.80797101 0.85144928 0.82789855 0.93478261 0.93478261] mean value: 0.8846014492753623 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.95652174 0.80769231 0.8 0.66666667 0.84 0.68965517 0.74074074 0.73333333 0.88461538 0.875 ] mean value: 0.799422534459266 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.24 MCC on Training: 0.77 Running classifier: 6 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.86984348 0.90739107 0.85374165 0.87888694 0.85046864 0.83007908 0.8632338 0.89739513 0.84550118 0.84741783] mean value: 0.8643958806991577 key: score_time value: [0.00920033 0.01295376 0.00981164 0.01013184 0.0090692 0.00927734 0.01023722 0.00938416 0.00952601 0.00926542] mean value: 0.009885692596435547 key: test_mcc value: [0.74773263 0.95825929 0.78804348 0.61775362 0.87318841 0.7085716 0.7023605 0.91833182 0.91304348 0.78334945] mean value: 0.8010634281188238 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.875 0.97777778 0.89361702 0.80851064 0.93617021 0.84444444 0.85714286 0.95652174 0.95652174 0.88888889] mean value: 0.8994595318855264 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.84 1. 0.875 0.79166667 0.95652174 0.9047619 0.84 1. 0.95652174 0.90909091] mean value: 0.907356295878035 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.91304348 0.95652174 0.91304348 0.82608696 0.91666667 0.79166667 0.875 0.91666667 0.95652174 0.86956522] mean value: 0.8934782608695653 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.87234043 0.9787234 0.89361702 0.80851064 0.93617021 0.85106383 0.85106383 0.95744681 0.95652174 0.89130435] mean value: 0.8996762257169287 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.87318841 0.97826087 0.89402174 0.80887681 0.9365942 0.85235507 0.85054348 0.95833333 0.95652174 0.89130435] mean value: 0.9000000000000001 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.77777778 0.95652174 0.80769231 0.67857143 0.88 0.73076923 0.75 0.91666667 0.91666667 0.8 ] mean value: 0.8214665817274514 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.01 MCC on Training: 0.8 Running classifier: 7 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.00949192 0.00940037 0.01014972 0.00998282 0.00955248 0.00936103 0.0097816 0.00992203 0.00979114 0.00963092] mean value: 0.009706401824951172 key: score_time value: [0.00924778 0.00908899 0.00877357 0.00869465 0.00902772 0.00896478 0.00889587 0.00860023 0.00880003 0.00922942] mean value: 0.00893230438232422 key: test_mcc value: [ 0.38259687 -0.02173913 0.23593505 0.15834857 0.44646172 0.1918812 0.36116212 0.53734864 0.26726124 0.27386128] mean value: 0.283311755819608 key: train_mcc value: [0.35186937 0.37707735 0.38062503 0.37941491 0.36849586 0.38100352 0.36232872 0.35729266 0.34406073 0.36731274] mean value: 0.3669480892966445 key: test_fscore value: [0.71698113 0.47826087 0.625 0.61538462 0.73469388 0.64150943 0.69387755 0.75555556 0.66666667 0.67924528] mean value: 0.6607174984800088 key: train_fscore value: [0.70386266 0.7092511 0.70824053 0.70428894 0.70484581 0.70693512 0.69933185 0.69642857 0.69042316 0.69955157] mean value: 0.7023159325930504 key: test_precision value: [0.63333333 0.47826087 0.6 0.55172414 0.72 0.5862069 0.68 0.80952381 0.60714286 0.6 ] mean value: 0.6266191904047974 key: train_precision value: [0.64313725 0.66255144 0.66806723 0.67241379 0.6557377 0.66666667 0.65690377 0.65546218 0.6512605 0.66382979] mean value: 0.6596030328810132 key: test_recall value: [0.82608696 0.47826087 0.65217391 0.69565217 0.75 0.70833333 0.70833333 0.70833333 0.73913043 0.7826087 ] mean value: 0.7048913043478261 key: train_recall value: [0.77725118 0.76303318 0.7535545 0.73933649 0.76190476 0.75238095 0.74761905 0.74285714 0.73459716 0.73933649] mean value: 0.7511870909501241 key: test_accuracy value: [0.68085106 0.4893617 0.61702128 0.57446809 0.72340426 0.59574468 0.68085106 0.76595745 0.63043478 0.63043478] mean value: 0.6388529139685476 key: train_accuracy value: [0.67220903 0.68646081 0.6888361 0.6888361 0.68171021 0.6888361 0.67933492 0.67695962 0.67061611 0.68246445] mean value: 0.6816263466582612 key: test_roc_auc value: [0.68387681 0.48913043 0.61775362 0.57699275 0.72282609 0.5932971 0.68025362 0.76721014 0.63043478 0.63043478] mean value: 0.6392210144927535 key: train_roc_auc value: [0.67195893 0.68627849 0.68868201 0.68871587 0.68190025 0.68898668 0.67949673 0.67711578 0.67061611 0.68246445] mean value: 0.6816215301286391 key: test_jcc value: [0.55882353 0.31428571 0.45454545 0.44444444 0.58064516 0.47222222 0.53125 0.60714286 0.5 0.51428571] mean value: 0.49776450976284947 key: train_jcc value: [0.54304636 0.54948805 0.54827586 0.54355401 0.54421769 0.5467128 0.53767123 0.53424658 0.52721088 0.53793103] mean value: 0.5412354498159684 MCC on Blind test: -0.1 MCC on Training: 0.28 Running classifier: 8 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.10955 0.16310787 0.12945127 0.17684841 0.12497854 0.07421994 0.12003326 0.07374811 0.13261342 0.12032437] mean value: 0.12248752117156983 key: score_time value: [0.02457738 0.02389956 0.0245769 0.03097796 0.01421976 0.0142591 0.01417327 0.01424575 0.02250266 0.02271605] mean value: 0.02061483860015869 key: test_mcc value: [0.79418308 0.62296012 0.54621844 0.33346345 0.78804348 0.55422693 0.66121206 0.53483083 0.75056834 0.69631062] mean value: 0.6282017337346536 key: train_mcc value: [0.92007277 0.92048742 0.91097199 0.93000284 0.93387521 0.94801357 0.94367934 0.9290068 0.92975612 0.93017484] mean value: 0.9296040886134582 key: test_fscore value: [0.89795918 0.81632653 0.78431373 0.69230769 0.89361702 0.8 0.84 0.78431373 0.88 0.85106383] mean value: 0.8239901708637628 key: train_fscore value: [0.96055684 0.96073903 0.95612009 0.96535797 0.96713615 0.97411765 0.97196262 0.96470588 0.96519722 0.96535797] mean value: 0.9651251414530483 key: test_precision value: [0.84615385 0.76923077 0.71428571 0.62068966 0.91304348 0.70967742 0.80769231 0.74074074 0.81481481 0.83333333] mean value: 0.7769662079039648 key: train_precision value: [0.94090909 0.93693694 0.93243243 0.94144144 0.9537037 0.9627907 0.95412844 0.95348837 0.94545455 0.94144144] mean value: 0.9462727102454007 key: test_recall value: [0.95652174 0.86956522 0.86956522 0.7826087 0.875 0.91666667 0.875 0.83333333 0.95652174 0.86956522] mean value: 0.8804347826086957 key: train_recall value: [0.98104265 0.98578199 0.98104265 0.99052133 0.98095238 0.98571429 0.99047619 0.97619048 0.98578199 0.99052133] mean value: 0.9848025276461294 key: test_accuracy value: [0.89361702 0.80851064 0.76595745 0.65957447 0.89361702 0.76595745 0.82978723 0.76595745 0.86956522 0.84782609] mean value: 0.8100370027752082 key: train_accuracy value: [0.95961995 0.95961995 0.95486936 0.96437055 0.96674584 0.97387173 0.97149644 0.96437055 0.96445498 0.96445498] mean value: 0.9643874323152953 key: test_roc_auc value: [0.89492754 0.80978261 0.76811594 0.66213768 0.89402174 0.76268116 0.82880435 0.76449275 0.86956522 0.84782609] mean value: 0.8102355072463767 key: train_roc_auc value: [0.95956895 0.95955766 0.95480704 0.96430828 0.96677951 0.9738998 0.97154141 0.96439856 0.96445498 0.96445498] mean value: 0.96437711577522 key: test_jcc value: [0.81481481 0.68965517 0.64516129 0.52941176 0.80769231 0.66666667 0.72413793 0.64516129 0.78571429 0.74074074] mean value: 0.7049156264428135 key: train_jcc value: [0.92410714 0.92444444 0.9159292 0.93303571 0.93636364 0.94954128 0.94545455 0.93181818 0.93273543 0.93303571] mean value: 0.9326465293461839 MCC on Blind test: 0.29 MCC on Training: 0.63 Running classifier: 9 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.03023624 0.01018214 0.01049352 0.01015139 0.01009607 0.01004457 0.01080632 0.01091886 0.00962687 0.0109942 ] mean value: 0.01235501766204834 key: score_time value: [0.01902819 0.0136323 0.01739502 0.01426792 0.01339602 0.01464581 0.01692724 0.01454091 0.01393795 0.01758838] mean value: 0.015535974502563476 key: test_mcc value: [0.7085716 0.74456522 0.50857834 0.45948781 0.7085716 0.45455353 0.61706091 0.53483083 0.56694671 0.74194083] mean value: 0.6045107363188114 key: train_mcc value: [0.69460334 0.72914668 0.72325683 0.76222145 0.71283276 0.74841367 0.71456339 0.7042937 0.72031459 0.71158409] mean value: 0.7221230491569898 key: test_fscore value: [0.85714286 0.86956522 0.76923077 0.74509804 0.84444444 0.75471698 0.81632653 0.78431373 0.8 0.875 ] mean value: 0.8115838564659578 key: train_fscore value: [0.85327314 0.86995516 0.86681716 0.88539326 0.86222222 0.87837838 0.86230248 0.85714286 0.86547085 0.86160714] mean value: 0.8662562644519879 key: test_precision value: [0.80769231 0.86956522 0.68965517 0.67857143 0.9047619 0.68965517 0.8 0.74074074 0.6875 0.84 ] mean value: 0.7708141943985272 key: train_precision value: [0.81465517 0.82553191 0.82758621 0.84188034 0.80833333 0.83333333 0.81974249 0.81818182 0.8212766 0.81434599] mean value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( 0.8224867197509036 key: test_recall value: [0.91304348 0.86956522 0.86956522 0.82608696 0.79166667 0.83333333 0.83333333 0.83333333 0.95652174 0.91304348] mean value: 0.8639492753623189 key: train_recall value: [0.8957346 0.91943128 0.90995261 0.93364929 0.92380952 0.92857143 0.90952381 0.9 0.91469194 0.91469194] mean value: 0.9150056420672537 key: test_accuracy value: [0.85106383 0.87234043 0.74468085 0.72340426 0.85106383 0.72340426 0.80851064 0.76595745 0.76086957 0.86956522] mean value: 0.7970860314523589 key: train_accuracy value: [0.8456057 0.86223278 0.85985748 0.87885986 0.85273159 0.87173397 0.85510689 0.85035629 0.85781991 0.85308057] mean value: 0.8587385034503721 key: test_roc_auc value: [0.85235507 0.87228261 0.74728261 0.72554348 0.85235507 0.72101449 0.80797101 0.76449275 0.76086957 0.86956522] mean value: 0.7973731884057972 key: train_roc_auc value: [0.84548635 0.86209659 0.85973821 0.87872941 0.85290002 0.87186865 0.85523584 0.85047393 0.85781991 0.85308057] mean value: 0.858742947415933 key: test_jcc value: [0.75 0.76923077 0.625 0.59375 0.73076923 0.60606061 0.68965517 0.64516129 0.66666667 0.77777778] mean value: 0.6854071513241424 key: train_jcc value: [0.74409449 0.76984127 0.76494024 0.79435484 0.7578125 0.78313253 0.75793651 0.75 0.76284585 0.75686275] mean value: 0.7641820968741148 MCC on Blind test: 0.15 MCC on Training: 0.6 Running classifier: 10 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.04052258 0.08568478 0.10642672 0.06147552 0.08836508 0.05475545 0.06935763 0.03934407 0.0389359 0.0508976 ] mean value: 0.06357653141021728 key: score_time value: [0.02239037 0.01632404 0.02832556 0.02143645 0.02657485 0.02290225 0.01227331 0.01226044 0.01222491 0.01204538] mean value: 0.018675756454467774 key: test_mcc value: [0.70289855 0.59613578 0.59613578 0.53734864 0.65942029 0.36116212 0.4899891 0.66801039 0.76564149 0.62360956] mean value: 0.6000351705232285 key: train_mcc value: [0.84857958 0.85106781 0.87746085 0.87251824 0.84860683 0.88173145 0.86303502 0.84833508 0.84971652 0.89148598] mean value: 0.8632537363105109 key: test_fscore value: [0.85106383 0.80769231 0.80769231 0.7755102 0.83333333 0.69387755 0.76 0.84615385 0.88461538 0.82352941] mean value: 0.808346817614116 key: train_fscore value: [0.9255814 0.92727273 0.93981481 0.93735499 0.92523364 0.94145199 0.93240093 0.92488263 0.92626728 0.94638695] mean value: 0.9326647350329432 key: test_precision value: [0.83333333 0.72413793 0.72413793 0.73076923 0.83333333 0.68 0.73076923 0.78571429 0.79310345 0.75 ] mean value: 0.7585298724264242 key: train_precision value: [0.9086758 0.89082969 0.91855204 0.91818182 0.90825688 0.92626728 0.91324201 0.91203704 0.90134529 0.93119266] mean value: 0.9128580507830486 key: test_recall value: [0.86956522 0.91304348 0.91304348 0.82608696 0.83333333 0.70833333 0.79166667 0.91666667 1. 0.91304348] mean value: 0.8684782608695653 key: train_recall value: [0.94312796 0.96682464 0.96208531 0.95734597 0.94285714 0.95714286 0.95238095 0.93809524 0.95260664 0.96208531] mean value: 0.9534552019860077 key: test_accuracy value: [0.85106383 0.78723404 0.78723404 0.76595745 0.82978723 0.68085106 0.74468085 0.82978723 0.86956522 0.80434783] mean value: 0.7950508788159112 key: train_accuracy value: [0.9239905 0.9239905 0.93824228 0.93586698 0.9239905 0.94061758 0.93111639 0.9239905 0.92417062 0.94549763] mean value: 0.9311473472098705 key: test_roc_auc value: [0.85144928 0.78985507 0.78985507 0.76721014 0.82971014 0.68025362 0.74365942 0.82789855 0.86956522 0.80434783] mean value: 0.7953804347826088 key: train_roc_auc value: [0.92394493 0.92388851 0.93818551 0.93581584 0.92403521 0.94065674 0.93116678 0.92402392 0.92417062 0.94549763] mean value: 0.9311385691717445 key: test_jcc value: [0.74074074 0.67741935 0.67741935 0.63333333 0.71428571 0.53125 0.61290323 0.73333333 0.79310345 0.7 ] mean value: 0.6813788505452856 key: train_jcc value: [0.86147186 0.86440678 0.88646288 0.88209607 0.86086957 0.88938053 0.87336245 0.86026201 0.86266094 0.89823009] mean value: 0.8739203176138842 MCC on Blind test: -0.27 MCC on Training: 0.6 Running classifier: 11 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.03687644 0.03758669 0.03739309 0.03713179 0.03743529 0.03724146 0.0380435 0.03793001 0.03795695 0.0376761 ] mean value: 0.03752713203430176 key: score_time value: [0.01195025 0.01181555 0.01194215 0.01201105 0.01197004 0.0119164 0.01183605 0.01186299 0.01186442 0.01205301] mean value: 0.011922192573547364 key: test_mcc value: [0.57427536 0.53734864 0.57713344 0.27717391 0.61706091 0.48913043 0.36116212 0.44746377 0.67556602 0.69631062] mean value: 0.5252625231393825 key: train_mcc value: [0.67698107 0.69181104 0.72008957 0.70163264 0.66796906 0.7108915 0.69129032 0.7007922 0.71129201 0.71567196] mean value: 0.6988421369312487 key: test_fscore value: [0.7826087 0.7755102 0.79166667 0.63829787 0.81632653 0.75 0.69387755 0.72340426 0.84615385 0.85106383] mean value: 0.7668909451633781 key: train_fscore value: [0.83962264 0.84918794 0.86247086 0.85450346 0.8364486 0.85780886 0.8463357 0.85106383 0.85780886 0.85849057] mean value: 0.8513741310191387 key: test_precision value: [0.7826087 0.73076923 0.76 0.625 0.8 0.75 0.68 0.73913043 0.75862069 0.83333333] mean value: 0.7459462384192519 key: train_precision value: [0.83568075 0.83181818 0.84862385 0.83333333 0.82110092 0.84018265 0.84037559 0.84507042 0.8440367 0.85446009] mean value: 0.8394682485903344 key: test_recall value: [0.7826087 0.82608696 0.82608696 0.65217391 0.83333333 0.75 0.70833333 0.70833333 0.95652174 0.86956522] mean value: 0.791304347826087 key: train_recall value: [0.8436019 0.86729858 0.87677725 0.87677725 0.85238095 0.87619048 0.85238095 0.85714286 0.87203791 0.86255924] mean value: 0.863714737079666 key: test_accuracy value: [0.78723404 0.76595745 0.78723404 0.63829787 0.80851064 0.74468085 0.68085106 0.72340426 0.82608696 0.84782609] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( mean value: 0.7610083256244218 key: train_accuracy value: [0.83847981 0.8456057 0.85985748 0.85035629 0.83372922 0.85510689 0.8456057 0.85035629 0.85545024 0.85781991] mean value: 0.8492367529353493 key: test_roc_auc value: [0.78713768 0.76721014 0.78804348 0.63858696 0.80797101 0.74456522 0.68025362 0.72373188 0.82608696 0.84782609] mean value: 0.7611413043478261 key: train_roc_auc value: [0.83846761 0.84555405 0.8598172 0.85029339 0.83377341 0.85515685 0.84562176 0.85037238 0.85545024 0.85781991] mean value: 0.849232678853532 key: test_jcc value: [0.64285714 0.63333333 0.65517241 0.46875 0.68965517 0.6 0.53125 0.56666667 0.73333333 0.74074074] mean value: 0.6261758803138113 key: train_jcc value: [0.72357724 0.73790323 0.75819672 0.74596774 0.7188755 0.75102041 0.73360656 0.74074074 0.75102041 0.75206612] mean value: 0.7412974656980602 MCC on Blind test: -0.06 MCC on Training: 0.53 Running classifier: 12 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(cv=3, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(cv=3, random_state=42))]) key: fit_time value: [0.52031946 0.48446774 0.61795068 0.66369152 0.51609921 0.51158762 0.49263406 0.59106374 0.50030804 0.5177567 ] mean value: 0.5415878772735596 key: score_time value: [0.0121541 0.01223493 0.01212955 0.0124476 0.0119946 0.0122025 0.01221418 0.01223516 0.01216173 0.01199651] mean value: 0.012177085876464844 key: test_mcc value: [0.78804348 0.63294907 0.78804348 0.44746377 0.79418308 0.36231884 0.53483083 0.7070024 0.71269665 0.67556602] mean value: 0.6443097605200881 key: train_mcc value: [0.91452818 0.82078713 0.84398553 0.91949435 0.90049664 0.95253572 0.88663583 0.88699922 0.82487618 0.91944161] mean value: 0.886978037891606 key: test_fscore value: [0.89361702 0.82352941 0.89361702 0.72340426 0.88888889 0.68085106 0.78431373 0.8627451 0.8627451 0.84615385] mean value: 0.8259865430078195 key: train_fscore value: [0.95754717 0.9124424 0.92343387 0.96018735 0.95058824 0.97607656 0.94392523 0.94418605 0.91334895 0.95961995] mean value: 0.944135576356906 key: test_precision value: [0.875 0.75 0.875 0.70833333 0.95238095 0.69565217 0.74074074 0.81481481 0.78571429 0.75862069] mean value: 0.7956256990552343 key: train_precision value: [0.95305164 0.88789238 0.90454545 0.94907407 0.93953488 0.98076923 0.9266055 0.92272727 0.90277778 0.96190476] mean value: 0.9328882979980762 key: test_recall value: [0.91304348 0.91304348 0.91304348 0.73913043 0.83333333 0.66666667 0.83333333 0.91666667 0.95652174 0.95652174] mean value: 0.8641304347826086 key: train_recall value: [0.96208531 0.93838863 0.94312796 0.97156398 0.96190476 0.97142857 0.96190476 0.96666667 0.92417062 0.95734597] mean value: 0.9558587226359737 key: test_accuracy value: [0.89361702 0.80851064 0.89361702 0.72340426 0.89361702 0.68085106 0.76595745 0.85106383 0.84782609 0.82608696] mean value: 0.81845513413506 key: train_accuracy value: [0.95724466 0.90973872 0.9216152 0.95961995 0.95011876 0.97624703 0.94299287 0.94299287 0.91232227 0.95971564] mean value: 0.943260798595085 key: test_roc_auc value: [0.89402174 0.81068841 0.89402174 0.72373188 0.89492754 0.68115942 0.76449275 0.84963768 0.84782609 0.82608696] mean value: 0.818659420289855 key: train_roc_auc value: [0.95723313 0.9096705 0.92156398 0.95959151 0.95014669 0.97623561 0.94303769 0.94304897 0.91232227 0.95971564] mean value: 0.9432566012186865 key: test_jcc value: [0.80769231 0.7 0.80769231 0.56666667 0.8 0.51612903 0.64516129 0.75862069 0.75862069 0.73333333] mean value: 0.7093916317275606 key: train_jcc value: [0.91855204 0.83898305 0.85775862 0.92342342 0.9058296 0.95327103 0.89380531 0.89427313 0.84051724 0.92237443] mean value: 0.894878786370044 MCC on Blind test: 0.03 MCC on Training: 0.64 Running classifier: 13 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [0.94789052 1.90375662 1.81630301 1.83829761 1.73291302 2.01857781 1.87643886 1.62346268 1.3009553 2.05513024] mean value: 1.7113725662231445 key: score_time value: [0.01237488 0.01310802 0.01303029 0.01307201 0.01215339 0.0132103 0.01215792 0.01303816 0.01216602 0.01224756] mean value: 0.012655854225158691 key: test_mcc value: [0.62296012 0.7085716 0.7085716 0.4899891 0.70289855 0.66121206 0.62091661 0.66121206 0.69560834 0.82922798] mean value: 0.6701168025665882 key: train_mcc value: [0.74574606 0.96684273 0.91551308 0.90044114 0.9478412 0.98575942 0.96203932 0.89088998 0.87395899 0.99052133] mean value: 0.9179553234233163 key: test_fscore value: [0.81632653 0.85714286 0.85714286 0.72727273 0.85106383 0.84 0.82352941 0.84 0.85185185 0.91666667] mean value: 0.8380996732241146 key: train_fscore value: [0.87804878 0.98352941 0.95833333 0.945 0.97399527 0.99287411 0.98104265 0.94570136 0.9375 0.99526066] mean value: 0.9591285581718723 key: test_precision value: [0.76923077 0.80769231 0.80769231 0.76190476 0.86956522 0.80769231 0.77777778 0.80769231 0.74193548 0.88 ] mean value: 0.8031183240944813 key: train_precision value: [0.825 0.97663551 0.93665158 1. 0.96713615 0.99052133 0.97641509 0.90086207 0.88607595 0.99526066] mean value: 0.9454558351157395 key: test_recall value: [0.86956522 0.91304348 0.91304348 0.69565217 0.83333333 0.875 0.875 0.875 1. 0.95652174] mean value: 0.8806159420289855 key: train_recall value: [0.93838863 0.99052133 0.98104265 0.8957346 0.98095238 0.9952381 0.98571429 0.9952381 0.99526066 0.99526066] mean value: 0.9753351387948545 key: test_accuracy value: [0.80851064 0.85106383 0.85106383 0.74468085 0.85106383 0.82978723 0.80851064 0.82978723 0.82608696 0.91304348] mean value: 0.8313598519888992 key: train_accuracy value: [0.86935867 0.98337292 0.95724466 0.94774347 0.97387173 0.99287411 0.98099762 0.94299287 0.93364929 0.99526066] mean value: 0.957736600961376 key: test_roc_auc value: [0.80978261 0.85235507 0.85235507 0.74365942 0.85144928 0.82880435 0.80706522 0.82880435 0.82608696 0.91304348] mean value: 0.831340579710145 key: train_roc_auc value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( [0.86919431 0.9833559 0.95718799 0.9478673 0.97388851 0.99287971 0.9810088 0.94311668 0.93364929 0.99526066] mean value: 0.9577409162717219 key: test_jcc value: [0.68965517 0.75 0.75 0.57142857 0.74074074 0.72413793 0.7 0.72413793 0.74193548 0.84615385] mean value: 0.7238189676676885 key: train_jcc value: [0.7826087 0.96759259 0.92 0.8957346 0.94930876 0.98584906 0.9627907 0.89699571 0.88235294 0.99056604] mean value: 0.9233799082506552 MCC on Blind test: 0.07 MCC on Training: 0.67 Running classifier: 14 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.01326013 0.01311684 0.01049256 0.00956059 0.00945449 0.00915861 0.00943303 0.00930953 0.00936675 0.00928545] mean value: 0.010243797302246093 key: score_time value: [0.01177216 0.01057291 0.00902367 0.0085094 0.00858665 0.00857067 0.00857806 0.00844407 0.00869513 0.00865507] mean value: 0.009140777587890624 key: test_mcc value: [0.19490273 0.28602655 0.30500678 0.06776966 0.48913043 0.01386141 0.06089597 0.28051421 0.36514837 0.40533961] mean value: 0.2468595730474991 key: train_mcc value: [0.2863622 0.28273594 0.30473948 0.31691647 0.27729893 0.29443992 0.31396165 0.30937592 0.30709664 0.28714967] mean value: 0.29800768332468863 key: test_fscore value: [0.6122449 0.66666667 0.69090909 0.56 0.75 0.59649123 0.57692308 0.62222222 0.71698113 0.73076923] mean value: 0.6523207545595119 key: train_fscore value: [0.66958425 0.67099567 0.67549669 0.68682505 0.66373626 0.66516854 0.67561521 0.67410714 0.67692308 0.67659574] mean value: 0.6735047638860802 key: test_precision value: [0.57692308 0.60714286 0.59375 0.51851852 0.75 0.51515152 0.53571429 0.66666667 0.63333333 0.65517241] mean value: 0.6052372667243355 key: train_precision value: [0.62195122 0.61752988 0.6322314 0.63095238 0.61632653 0.62978723 0.6371308 0.63445378 0.63114754 0.61389961] mean value: 0.6265410388639728 key: test_recall value: [0.65217391 0.73913043 0.82608696 0.60869565 0.75 0.70833333 0.625 0.58333333 0.82608696 0.82608696] mean value: 0.7144927536231884 key: train_recall value: [0.72511848 0.73459716 0.72511848 0.7535545 0.71904762 0.7047619 0.71904762 0.71904762 0.72985782 0.7535545 ] mean value: 0.728370570977206 key: test_accuracy value: [0.59574468 0.63829787 0.63829787 0.53191489 0.74468085 0.5106383 0.53191489 0.63829787 0.67391304 0.69565217] mean value: 0.6199352451433858 key: train_accuracy value: [0.64133017 0.63895487 0.65083135 0.65558195 0.63657957 0.64608076 0.65558195 0.65320665 0.65165877 0.63981043] mean value: 0.6469616462721348 key: test_roc_auc value: [0.59692029 0.64039855 0.64221014 0.53351449 0.74456522 0.50634058 0.5298913 0.63949275 0.67391304 0.69565217] mean value: 0.6202898550724638 key: train_roc_auc value: [0.64113067 0.63872715 0.65065448 0.65534868 0.63677499 0.64621981 0.65573234 0.65336267 0.65165877 0.63981043] mean value: 0.6469419995486347 key: test_jcc value: [0.44117647 0.5 0.52777778 0.38888889 0.6 0.425 0.40540541 0.4516129 0.55882353 0.57575758] mean value: 0.48744425510554545 key: train_jcc value: [0.50328947 0.50488599 0.51 0.52302632 0.49671053 0.4983165 0.51013514 0.50841751 0.51162791 0.51125402] mean value: 0.5077663377413307 MCC on Blind test: -0.31 MCC on Training: 0.25 Running classifier: 15 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.0110898 0.01083302 0.01088095 0.0109005 0.01072955 0.01007056 0.01071286 0.0098815 0.01074553 0.01102281] mean value: 0.010686707496643067 key: score_time value: [0.0095005 0.00955057 0.00967431 0.00896835 0.00958371 0.00964236 0.0087738 0.01013446 0.0084908 0.00955081] mean value: 0.009386968612670899 key: test_mcc value: [0.57427536 0.49819858 0.45948781 0.49183384 0.66243303 0.14889238 0.23747392 0.53483083 0.39735971 0.65217391] mean value: 0.4656959367971279 key: train_mcc value: [0.55459946 0.53726741 0.56154004 0.55979129 0.54010632 0.59480388 0.57698517 0.57378172 0.49697661 0.48582446] mean value: 0.5481676360378004 key: test_fscore value: [0.7826087 0.76 0.74509804 0.75 0.82608696 0.62962963 0.66666667 0.78431373 0.72 0.82608696] mean value: 0.7490490669697831 key: train_fscore value: [0.78440367 0.77927928 0.79475983 0.79295154 0.78222222 0.80542986 0.79820628 0.79262673 0.76169265 0.75395034] mean value: 0.7845522397729429 key: test_precision value: [0.7826087 0.7037037 0.67857143 0.72 0.86363636 0.56666667 0.6 0.74074074 0.66666667 0.82608696] mean value: 0.7148681222159483 key: train_precision value: [0.76 0.74248927 0.73684211 0.74074074 0.73333333 0.76724138 0.75423729 0.76785714 0.71848739 0.71982759] mean value: 0.7441056241191458 key: test_recall value: [0.7826087 0.82608696 0.82608696 0.7826087 0.79166667 0.70833333 0.75 0.83333333 0.7826087 0.82608696] mean value: 0.7909420289855074 key: train_recall value: [0.81042654 0.81990521 0.86255924 0.85308057 0.83809524 0.84761905 0.84761905 0.81904762 0.81042654 0.79146919] mean value: 0.830024825095915 key: test_accuracy value: [0.78723404 0.74468085 0.72340426 0.74468085 0.82978723 0.57446809 0.61702128 0.76595745 0.69565217 0.82608696] mean value: 0.7308973172987974 key: train_accuracy value: [0.77672209 0.7672209 0.77672209 0.77672209 0.7672209 0.79572447 0.78622328 0.78622328 0.7464455 0.74170616] mean value: 0.7720930756154946 key: test_roc_auc value: [0.78713768 0.74637681 0.72554348 0.74547101 0.83061594 0.57155797 0.61413043 0.76449275 0.69565217 0.82608696] mean value: 0.7307065217391304 key: train_roc_auc value: [0.77664184 0.76709546 0.77651772 0.77654028 0.76738885 0.79584744 0.78636877 0.78630106 0.7464455 0.74170616] mean value: 0.772085308056872 key: test_jcc value: [0.64285714 0.61290323 0.59375 0.6 0.7037037 0.45945946 0.5 0.64516129 0.5625 0.7037037 ] mean value: 0.6024038525853042 key: train_jcc value: [0.64528302 0.63837638 0.65942029 0.65693431 0.64233577 0.67424242 0.6641791 0.65648855 0.61510791 0.60507246] mean value: 0.6457440221255073 MCC on Blind test: 0.18 MCC on Training: 0.47 Running classifier: 16 /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.01411843 0.01650715 0.01883101 0.01977611 0.02414346 0.02910447 0.02405095 0.02312279 0.02294278 0.02035236] mean value: 0.02129495143890381 key: score_time value: [0.00901842 0.01185656 0.011832 0.01196575 0.01204824 0.01203036 0.01208806 0.01201677 0.01202726 0.01199913] mean value: 0.01168825626373291 key: test_mcc value: [0.50052164 0.44337478 0.50321854 0.30093209 0.66534784 0.32123465 0.41502398 0.45948781 0.52623481 0.62764591] mean value: 0.4763022054714249 key: train_mcc value: [0.45466717 0.46089829 0.66882657 0.51482114 0.58283066 0.77281723 0.6305944 0.67881804 0.65204696 0.64224175] mean value: 0.6058562198875148 key: test_fscore value: [0.62857143 0.74193548 0.7 0.7 0.84210526 0.65217391 0.59459459 0.69767442 0.74418605 0.82142857] mean value: 0.7122669719783215 key: train_fscore value: [0.53924915 0.75774135 0.80636605 0.77819549 0.8030888 0.88292683 0.73432836 0.80434783 0.78804348 0.83193277] mean value: 0.7726220099153245 key: test_precision value: [0.91666667 0.58974359 0.82352941 0.56756757 0.72727273 0.68181818 0.84615385 0.78947368 0.8 0.6969697 ] mean value: 0.7439195372167509 key: train_precision value: [0.96341463 0.61538462 0.91566265 0.64485981 0.67532468 0.905 0.984 0.93670886 0.92356688 0.74716981] mean value: 0.8311091939603295 key: test_recall value: [0.47826087 1. 0.60869565 0.91304348 1. 0.625 0.45833333 0.625 0.69565217 1. ] mean value: 0.7403985507246377 key: train_recall value: [0.37440758 0.98578199 0.72037915 0.98104265 0.99047619 0.86190476 0.58571429 0.7047619 0.68720379 0.93838863] mean value: 0.7830060934326337 key: test_accuracy value: [0.72340426 0.65957447 0.74468085 0.61702128 0.80851064 0.65957447 0.68085106 0.72340426 0.76086957 0.7826087 ] mean value: 0.716049953746531 key: train_accuracy value: [0.67933492 0.68408551 0.82660333 0.71971496 0.75771971 0.88598575 0.78859857 0.82897862 0.81516588 0.81042654] mean value: 0.779661379473382 key: test_roc_auc value: [0.7182971 0.66666667 0.74184783 0.62318841 0.80434783 0.66032609 0.68568841 0.72554348 0.76086957 0.7826087 ] mean value: 0.7169384057971014 key: train_roc_auc value: [0.68006093 0.68336719 0.82685624 0.71909276 0.75827127 0.88592868 0.78811781 0.82868427 0.81516588 0.81042654] mean value: 0.7795971563981043 key: test_jcc value: [0.45833333 0.58974359 0.53846154 0.53846154 0.72727273 0.48387097 0.42307692 0.53571429 0.59259259 0.6969697 ] mean value: 0.5584497193368161 key: train_jcc value: [0.36915888 0.60997067 0.67555556 0.63692308 0.67096774 0.79039301 0.58018868 0.67272727 0.65022422 0.71223022] mean value: 0.6368339323552561 MCC on Blind test: -0.24 MCC on Training: 0.48 Running classifier: 17 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.02715826 0.02869511 0.02865434 0.04262924 0.02857614 0.02761865 0.02892327 0.03102922 0.02917218 0.02859974] mean value: 0.03010561466217041 key: score_time value: [0.01242328 0.01329803 0.01246595 0.0124259 0.01260948 0.01259637 0.01271319 0.01256251 0.01266313 0.01260543] mean value: 0.012636327743530273 key: test_mcc value: [1. 0.87979456 0.95833333 0.95833333 0.95833333 0.95825929 0.95833333 0.95825929 1. 0.95742711] mean value: 0.9587073580339226 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 0.93877551 0.9787234 0.9787234 0.9787234 0.97959184 0.9787234 0.97959184 1. 0.97777778] mean value: 0.9790630578472523 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.88461538 0.95833333 0.95833333 1. 0.96 1. 0.96 1. 1. ] mean value: 0.9721282051282051 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 0.95833333 1. 0.95833333 1. 1. 0.95652174] mean value: 0.9873188405797103 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 0.93617021 0.9787234 0.9787234 0.9787234 0.9787234 0.9787234 0.9787234 1. 0.97826087] mean value: 0.9786771507863088 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 0.9375 0.97916667 0.97916667 0.97916667 0.97826087 0.97916667 0.97826087 1. 0.97826087] mean value: 0.9788949275362319 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 0.88461538 0.95833333 0.95833333 0.95833333 0.96 0.95833333 0.96 1. 0.95652174] mean value: 0.9594470457079153 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.11 MCC on Training: 0.96 Running classifier: 18 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42))]) key: fit_time value: [0.70596194 0.67194986 0.68256259 0.69296789 0.69625568 0.71055293 0.67071724 0.64607167 0.68631053 0.70024133] mean value: 0.686359167098999 key: score_time value: [0.14330006 0.20726705 0.17665982 0.16223335 0.14976692 0.28783274 0.15997791 0.16781712 0.19066858 0.14900398] mean value: 0.1794527530670166 key: test_mcc value: [0.91804649 0.74456522 0.83243502 0.5326087 0.87979456 0.61706091 0.61706091 0.7070024 0.87038828 0.82922798] mean value: 0.7548190452093155 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.95454545 0.86956522 0.90909091 0.76595745 0.93333333 0.81632653 0.81632653 0.8627451 0.93617021 0.90909091] mean value: 0.8773151642290085 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.86956522 0.95238095 0.75 1. 0.8 0.8 0.81481481 0.91666667 0.95238095] mean value: 0.8855808603634691 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.91304348 0.86956522 0.86956522 0.7826087 0.875 0.83333333 0.83333333 0.91666667 0.95652174 0.86956522] mean value: 0.8719202898550724 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.95744681 0.87234043 0.91489362 0.76595745 0.93617021 0.80851064 0.80851064 0.85106383 0.93478261 0.91304348] mean value: 0.8762719703977799 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.95652174 0.87228261 0.91394928 0.76630435 0.9375 0.80797101 0.80797101 0.84963768 0.93478261 0.91304348] mean value: 0.8759963768115941 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.91304348 0.76923077 0.83333333 0.62068966 0.875 0.68965517 0.68965517 0.75862069 0.88 0.83333333] mean value: 0.7862561603813478 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.01 MCC on Training: 0.75 Running classifier: 19 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea...age_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42))]) key: fit_time value: [1.00020933 0.99791741 1.01137686 1.11834192 0.96931624 1.05144382 1.03946328 1.01004028 1.00286293 0.9766748 ] mean value: 1.017764687538147 key: score_time value: [0.18322039 0.26331258 0.25167084 0.18763065 0.2774899 0.2495203 0.25441551 0.22894025 0.24577665 0.22858691] mean value: 0.23705639839172363 key: test_mcc value: [0.83243502 0.74682354 0.7876601 0.5326087 0.87979456 0.61775362 0.62091661 0.7023605 0.91304348 0.87705802] mean value: 0.751045413964842 key: train_mcc value: [0.96238859 0.94367934 0.95266794 0.96203932 0.9528778 0.95751219 0.96238354 0.96216707 0.95277786 0.97631428] mean value: 0.9584807912766319 key: test_fscore value: [0.90909091 0.86363636 0.88888889 0.76595745 0.93333333 0.80851064 0.82352941 0.85714286 0.95652174 0.93023256] mean value: 0.8736844146233411 key: train_fscore value: [0.98076923 0.97101449 0.97607656 0.98095238 0.97584541 0.97831325 0.98067633 0.98076923 0.97607656 0.98812352] mean value: 0.9788616952874225 key: test_precision value: [0.95238095 0.9047619 0.90909091 0.75 1. 0.82608696 0.77777778 0.84 0.95652174 1. ] mean value: 0.8916620239663718 key: train_precision value: [0.99512195 0.99014778 0.98550725 0.98564593 0.99019608 0.9902439 0.99509804 0.99029126 0.98550725 0.99047619] mean value: 0.9898235632936918 key: test_recall value: [0.86956522 0.82608696 0.86956522 0.7826087 0.875 0.79166667 0.875 0.875 0.95652174 0.86956522] mean value: 0.8590579710144925 key: train_recall value: [0.96682464 0.95260664 0.96682464 0.97630332 0.96190476 0.96666667 0.96666667 0.97142857 0.96682464 0.98578199] mean value: 0.9681832543443918 key: test_accuracy value: [0.91489362 0.87234043 0.89361702 0.76595745 0.93617021 0.80851064 0.80851064 0.85106383 0.95652174 0.93478261] mean value: 0.874236817761332 key: train_accuracy value: [0.98099762 0.97149644 0.97624703 0.98099762 0.97624703 0.97862233 0.98099762 0.98099762 0.97630332 0.98815166] mean value: 0.9791058301718994 key: test_roc_auc value: [0.91394928 0.87137681 0.89311594 0.76630435 0.9375 0.80887681 0.80706522 0.85054348 0.95652174 0.93478261] mean value: 0.8740036231884056 key: train_roc_auc value: [0.98103137 0.97154141 0.97626947 0.9810088 0.97621304 0.978594 0.98096367 0.98097495 0.97630332 0.98815166] mean value: 0.9791051681336043 key: test_jcc value: [0.83333333 0.76 0.8 0.62068966 0.875 0.67857143 0.7 0.75 0.91666667 0.86956522] mean value: 0.7803826301135147 key: train_jcc value: [0.96226415 0.94366197 0.95327103 0.96261682 0.95283019 0.95754717 0.96208531 0.96226415 0.95327103 0.97652582] mean value: 0.9586337640366134 MCC on Blind test: -0.01 MCC on Training: 0.75 Running classifier: 20 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.02817559 0.01457477 0.01473737 0.03208876 0.03718519 0.03821349 0.01544476 0.01480818 0.01477695 0.0289371 ] mean value: 0.023894214630126955 key: score_time value: [0.01252842 0.01181245 0.01196027 0.02028584 0.02203488 0.01187992 0.01176548 0.01211476 0.01183748 0.02249908] mean value: 0.014871859550476074 key: test_mcc value: [0.66243303 0.54621844 0.78804348 0.31884058 0.7023605 0.44646172 0.4899891 0.53176131 0.75056834 0.66226618] mean value: 0.5898942673940177 key: train_mcc value: [0.77319708 0.77031539 0.78769474 0.79829272 0.74515638 0.7877567 0.79595939 0.78237926 0.77421794 0.78287023] mean value: 0.7797839824034417 key: test_fscore value: [0.83333333 0.78431373 0.89361702 0.65217391 0.85714286 0.73469388 0.76 0.7755102 0.88 0.84 ] mean value: 0.8010784931919114 key: train_fscore value: [0.88940092 0.88888889 0.89655172 0.90205011 0.87557604 0.8960739 0.89882353 0.89302326 0.88990826 0.89351852] mean value: 0.8923815149074663 key: test_precision value: [0.8 0.71428571 0.875 0.65217391 0.84 0.72 0.73076923 0.76 0.81481481 0.77777778] mean value: 0.7684821450691015 key: train_precision value: [0.86547085 0.85217391 0.87053571 0.86842105 0.84821429 0.86995516 0.88837209 0.87272727 0.86222222 0.87330317] mean value: 0.8671395730037232 key: test_recall value: [0.86956522 0.86956522 0.91304348 0.65217391 0.875 0.75 0.79166667 0.79166667 0.95652174 0.91304348] mean value: 0.8382246376811594 key: train_recall value: [0.91469194 0.92890995 0.92417062 0.93838863 0.9047619 0.92380952 0.90952381 0.91428571 0.91943128 0.91469194] mean value: 0.9192665312570526 key: test_accuracy value: [0.82978723 0.76595745 0.89361702 0.65957447 0.85106383 0.72340426 0.74468085 0.76595745 0.86956522 0.82608696] mean value: 0.7929694727104533 key: train_accuracy value: [0.88598575 0.88361045 0.89311164 0.89786223 0.87173397 0.89311164 0.89786223 0.89073634 0.88625592 0.89099526] mean value: 0.8891265436615596 key: test_roc_auc value: [0.83061594 0.76811594 0.89402174 0.65942029 0.85054348 0.72282609 0.74365942 0.76539855 0.86956522 0.82608696] mean value: 0.7930253623188406 key: train_roc_auc value: [0.8859174 0.8835026 0.89303769 0.89776574 0.87181223 0.89318438 0.89788987 0.89079215 0.88625592 0.89099526] mean value: 0.8891153238546604 key: test_jcc value: [0.71428571 0.64516129 0.80769231 0.48387097 0.75 0.58064516 0.61290323 0.63333333 0.78571429 0.72413793] mean value: 0.6737744217221413 key: train_jcc value: [0.80082988 0.8 0.8125 0.82157676 0.77868852 0.81171548 0.81623932 0.80672269 0.80165289 0.80753138] mean value: 0.8057456923395929 MCC on Blind test: -0.15 MCC on Training: 0.59 Running classifier: 21 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=3))]) key: fit_time value: [0.05951238 0.05317283 0.09262514 0.11926723 0.15157723 0.12846446 0.13028646 0.11019468 0.128268 0.12258267] mean value: 0.10959510803222657 key: score_time value: [0.01185656 0.0118978 0.02401161 0.02290392 0.0329659 0.02152944 0.0221293 0.02259541 0.02118587 0.0213263 ] mean value: 0.02124021053314209 key: test_mcc value: [0.74773263 0.54621844 0.74773263 0.49183384 0.65942029 0.36265926 0.4899891 0.7070024 0.80178373 0.62360956] mean value: 0.6177981874429266 key: train_mcc value: [0.83447462 0.84152968 0.84436402 0.8644069 0.83649149 0.88173145 0.84860683 0.85352605 0.85524442 0.87369775] mean value: 0.853407322054173 key: test_fscore value: [0.875 0.78431373 0.875 0.75 0.83333333 0.70588235 0.76 0.8627451 0.90196078 0.82352941] mean value: 0.8171764705882353 key: train_fscore value: [0.9187935 0.92272727 0.92378753 0.93363844 0.91990847 0.94145199 0.92523364 0.92773893 0.92906178 0.93793103] mean value: 0.9280272598441902 key: test_precision value: [0.84 0.71428571 0.84 0.72 0.83333333 0.66666667 0.73076923 0.81481481 0.82142857 0.75 ] mean value: 0.7731298331298332 key: train_precision value: [0.9 0.88646288 0.9009009 0.90265487 0.88546256 0.92626728 0.90825688 0.9086758 0.89823009 0.91071429] mean value: 0.9027625540456242 key: test_recall value: [0.91304348 0.86956522 0.91304348 0.7826087 0.83333333 0.75 0.79166667 0.91666667 1. 0.91304348] mean value: 0.8682971014492754 key: train_recall value: [0.93838863 0.96208531 0.9478673 0.96682464 0.95714286 0.95714286 0.94285714 0.94761905 0.96208531 0.96682464] mean value: 0.9548837734145792 key: test_accuracy value: [0.87234043 0.76595745 0.87234043 0.74468085 0.82978723 0.68085106 0.74468085 0.85106383 0.89130435 0.80434783] mean value: 0.8057354301572618 key: train_accuracy value: [0.91686461 0.9192399 0.9216152 0.93111639 0.91686461 0.94061758 0.9239905 0.9263658 0.92654028 0.93601896] mean value: 0.9259233826029203 key: test_roc_auc value: [0.87318841 0.76811594 0.87318841 0.74547101 0.82971014 0.67934783 0.74365942 0.84963768 0.89130435 0.80434783] mean value: 0.8057971014492754 key: train_roc_auc value: [0.91681336 0.91913789 0.9215527 0.93103137 0.91696005 0.94065674 0.92403521 0.92641616 0.92654028 0.93601896] mean value: 0.925916271721959 key: test_jcc value: [0.77777778 0.64516129 0.77777778 0.6 0.71428571 0.54545455 0.61290323 0.75862069 0.82142857 0.7 ] mean value: 0.6953409592508591 key: train_jcc value: [0.84978541 0.85654008 0.8583691 0.87553648 0.85169492 0.88938053 0.86086957 0.86521739 0.86752137 0.88311688] mean value: 0.8658031724900328 MCC on Blind test: -0.1 MCC on Training: 0.62 Running classifier: 22 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.02732754 0.01938725 0.01839542 0.02201033 0.01872611 0.01850796 0.01894212 0.01916289 0.01883578 0.01904726] mean value: 0.020034265518188477 key: score_time value: [0.01634145 0.01169157 0.01130533 0.01221037 0.01256347 0.01145101 0.01139116 0.011729 0.01136589 0.01165247] mean value: 0.012170171737670899 key: test_mcc value: [0.5326087 0.61706091 0.57713344 0.32602701 0.66243303 0.36116212 0.44746377 0.53734864 0.71269665 0.73913043] mean value: 0.5513064694024388 key: train_mcc value: [0.7247771 0.715454 0.72066349 0.76728964 0.70594503 0.73936557 0.72008957 0.71972467 0.71090846 0.73025979] mean value: 0.725447732956481 key: test_fscore value: [0.76595745 0.8 0.79166667 0.68 0.82608696 0.69387755 0.72340426 0.75555556 0.8627451 0.86956522] mean value: 0.7768858747322549 key: train_fscore value: [0.86057692 0.86046512 0.86374134 0.88470588 0.84951456 0.86618005 0.85714286 0.85985748 0.85510689 0.86713287] mean value: 0.8624423967791491 key: test_precision value: [0.75 0.81818182 0.76 0.62962963 0.86363636 0.68 0.73913043 0.80952381 0.78571429 0.86956522] mean value: 0.770538155885982 key: train_precision value: [0.87317073 0.84474886 0.84234234 0.87850467 0.86633663 0.88557214 0.87192118 0.85781991 0.85714286 0.85321101] mean value: 0.8630770332157642 key: test_recall value: [0.7826087 0.7826087 0.82608696 0.73913043 0.79166667 0.70833333 0.70833333 0.70833333 0.95652174 0.86956522] mean value: 0.7873188405797101 key: train_recall value: [0.84834123 0.87677725 0.88625592 0.89099526 0.83333333 0.84761905 0.84285714 0.86190476 0.85308057 0.88151659] mean value: 0.8622681110358836 key: test_accuracy value: [0.76595745 0.80851064 0.78723404 0.65957447 0.82978723 0.68085106 0.72340426 0.76595745 0.84782609 0.86956522] mean value: 0.7738667900092506 key: train_accuracy value: [0.86223278 0.85748219 0.85985748 0.88361045 0.85273159 0.86935867 0.85985748 0.85985748 0.85545024 0.86492891] mean value: 0.8625367270434869 key: test_roc_auc value: [0.76630435 0.80797101 0.78804348 0.66123188 0.83061594 0.68025362 0.72373188 0.76721014 0.84782609 0.86956522] mean value: 0.7742753623188404 key: train_roc_auc value: [0.86226585 0.85743624 0.85979463 0.88359287 0.85268562 0.86930715 0.8598172 0.85986233 0.85545024 0.86492891] mean value: 0.8625141051681335 key: test_jcc value: [0.62068966 0.66666667 0.65517241 0.51515152 0.7037037 0.53125 0.56666667 0.60714286 0.75862069 0.76923077] mean value: 0.6394294937182867 key: train_jcc value: [0.75527426 0.75510204 0.7601626 0.79324895 0.73839662 0.7639485 0.75 0.75416667 0.74688797 0.7654321 ] mean value: 0.7582619703757126 MCC on Blind test: -0.02 MCC on Training: 0.55 Running classifier: 23 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.01487279 0.0199039 0.0195117 0.02109385 0.02054167 0.02413893 0.02352715 0.02400136 0.01857924 0.05143905] mean value: 0.02376096248626709 key: score_time value: [0.00919247 0.01079869 0.01121378 0.01151633 0.01153326 0.01155519 0.01156473 0.011585 0.01165557 0.01198626] mean value: 0.011260128021240235 key: test_mcc value: [0.62296012 0.4121128 0.55975995 0.26308009 0.54113221 0.25417993 0.45455353 0.44646172 0.38359764 0.43452409] mean value: 0.4372362083591164 key: train_mcc value: [0.64642489 0.72997618 0.69621952 0.62377236 0.38975121 0.72976913 0.68663128 0.75202667 0.47155956 0.52378783] mean value: 0.6249918642759863 key: test_fscore value: [0.81632653 0.72 0.79245283 0.47058824 0.62857143 0.55 0.75471698 0.73469388 0.5 0.61111111] mean value: 0.6578460994460678 key: train_fscore value: [0.8336933 0.87053571 0.85534591 0.72835821 0.44117647 0.84675325 0.85 0.87981859 0.54295533 0.61688312] mean value: 0.7465519894515649 key: test_precision value: [0.76923077 0.66666667 0.7 0.72727273 1. 0.6875 0.68965517 0.72 0.88888889 0.84615385] mean value: 0.7695368070626691 key: train_precision value: [0.76587302 0.82278481 0.76691729 0.98387097 0.96774194 0.93142857 0.75555556 0.83982684 0.9875 0.97938144] mean value: 0.8800880432568423 key: test_recall value: [0.86956522 0.7826087 0.91304348 0.34782609 0.45833333 0.45833333 0.83333333 0.75 0.34782609 0.47826087] mean value: 0.6239130434782608 key: train_recall value: [0.91469194 0.92417062 0.96682464 0.57819905 0.28571429 0.77619048 0.97142857 0.92380952 0.37440758 0.45023697] mean value: 0.7165673662830061 key: test_accuracy value: [0.80851064 0.70212766 0.76595745 0.61702128 0.72340426 0.61702128 0.72340426 0.72340426 0.65217391 0.69565217] mean value: 0.702867715078631 key: train_accuracy value: [0.81710214 0.86223278 0.83610451 0.78384798 0.63895487 0.85985748 0.82897862 0.87410926 0.68483412 0.72037915] mean value: 0.7906400918598238 key: test_roc_auc value: [0.80978261 0.70380435 0.76902174 0.61141304 0.72916667 0.62047101 0.72101449 0.72282609 0.65217391 0.69565217] mean value: 0.7035326086956522 key: train_roc_auc value: [0.81686978 0.86208531 0.83579327 0.78433762 0.63811781 0.85965922 0.82931618 0.87422704 0.68483412 0.72037915] mean value: 0.7905619498984428 key: test_jcc value: [0.68965517 0.5625 0.65625 0.30769231 0.45833333 0.37931034 0.60606061 0.58064516 0.33333333 0.44 ] mean value: 0.5013780258951283 key: train_jcc value: [0.71481481 0.77075099 0.74725275 0.57276995 0.28301887 0.73423423 0.73913043 0.7854251 0.37264151 0.44600939] mean value: 0.6166048040522767 MCC on Blind test: -0.29 MCC on Training: 0.44 Running classifier: 24 Model_name: XGBoost Model func: /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:463: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_CV['source_data'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:490: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_BT['source_data'] = 'BT' XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.11038399 0.10422015 0.10015297 0.10709548 0.10734653 0.09444571 0.12200189 0.10841274 0.09521699 0.10340858] mean value: 0.10526850223541259 key: score_time value: [0.01116514 0.01139498 0.01166153 0.01193714 0.01262879 0.01085067 0.01196837 0.01101279 0.01094651 0.01112294] mean value: 0.011468887329101562 key: test_mcc value: [0.83303222 0.7876601 0.82971014 0.4899891 0.79418308 0.70289855 0.61706091 0.82971014 0.82922798 0.82922798] mean value: 0.7542700216660162 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.91666667 0.88888889 0.91304348 0.72727273 0.88888889 0.85106383 0.81632653 0.91666667 0.91666667 0.90909091] mean value: 0.8744575252801763 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.88 0.90909091 0.91304348 0.76190476 0.95238095 0.86956522 0.8 0.91666667 0.88 0.95238095] mean value: 0.8835032938076417 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.95652174 0.86956522 0.91304348 0.69565217 0.83333333 0.83333333 0.83333333 0.91666667 0.95652174 0.86956522] mean value: 0.8677536231884059 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.91489362 0.89361702 0.91489362 0.74468085 0.89361702 0.85106383 0.80851064 0.91489362 0.91304348 0.91304348] mean value: 0.8762257169287697 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.91576087 0.89311594 0.91485507 0.74365942 0.89492754 0.85144928 0.80797101 0.91485507 0.91304348 0.91304348] mean value: 0.8762681159420289 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.84615385 0.8 0.84 0.57142857 0.8 0.74074074 0.68965517 0.84615385 0.84615385 0.83333333] mean value: 0.7813619356377977 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.24 MCC on Training: 0.75 Extracting tts_split_name: sl Total cols in each df: CV df: 8 metaDF: 17 Adding column: Model_name Total cols in bts df: BT_df: 8 First proceeding to rowbind CV and BT dfs: Final output should have: 25 columns Combinig 2 using pd.concat by row ~ rowbind Checking Dims of df to combine: Dim of CV: (24, 8) Dim of BT: (24, 8) 8 Number of Common columns: 8 These are: ['source_data', 'Precision', 'JCC', 'MCC', 'Recall', 'Accuracy', 'F1', 'ROC_AUC'] Concatenating dfs with different resampling methods [WF]: Split type: sl No. of dfs combining: 2 PASS: 2 dfs successfully combined nrows in combined_df_wf: 48 ncols in combined_df_wf: 8 PASS: proceeding to merge metadata with CV and BT dfs Adding column: Model_name ========================================================= SUCCESS: Ran multiple classifiers ======================================================= ============================================================== Running several classification models (n): 24 List of models: ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('Gaussian NB', GaussianNB()) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('K-Nearest Neighbors', KNeighborsClassifier()) ('LDA', LinearDiscriminantAnalysis()) ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(cv=3, random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Multinomial', MultinomialNB()) ('Naive Bayes', BernoulliNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=12, random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Random Forest', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42)) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=3)) ('SVC', SVC(random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=12, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ================================================================ Running classifier: 1 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... 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Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.3s Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.2s remaining: 0.4s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... ƒÒkUÐÚ͇Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.0s remaining: 1.9s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.0s remaining: 1.9s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.0s remaining: 2.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.0s remaining: 2.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.1s remaining: 0.4s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.0s remaining: 0.3s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.1s remaining: 2.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.1s remaining: 0.4s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.1s remaining: 2.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.1s remaining: 0.4s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.1s remaining: 2.2s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.1s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.1s remaining: 0.4s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.1s remaining: 0.4s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.1s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.1s remaining: 2.2s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.1s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.1s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.1s remaining: 2.3s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.1s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.1s remaining: 0.4s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.2s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.2s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.2s remaining: 0.4s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.2s remaining: 0.4s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.2s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.2s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.2s remaining: 2.4s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.2s remaining: 0.4s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.3s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.15661168 0.15775347 0.17311716 0.15973258 0.16284204 0.15698123 0.16255617 0.15998936 0.15982389 0.15913463] mean value: 0.16085422039031982 key: score_time value: [0.01604271 0.01596761 0.01733184 0.01701474 0.01547194 0.01637006 0.01498556 0.01528382 0.01628971 0.01622176] mean value: 0.01609797477722168 key: test_mcc value: [0.59613578 0.91833182 0.87979456 0.62296012 0.91804649 0.73387289 0.69937861 0.84147165 0.83887049 0.80178373] mean value: 0.7850646137306149 key: train_mcc value: [0.95266368 0.96238354 0.97652111 0.98117384 0.97652373 0.97189305 0.97189305 0.97189305 0.98108673 0.96735713] mean value: 0.9713388895737808 key: test_fscore value: [0.80769231 0.95833333 0.93877551 0.81632653 0.96 0.87272727 0.85714286 0.92307692 0.92 0.90196078] mean value: 0.8956035519102746 key: train_fscore value: [0.97652582 0.98130841 0.9882904 0.99061033 0.98823529 0.98591549 0.98591549 0.98591549 0.99056604 0.98368298] mean value: 0.9856965753985877 key: test_precision value: [0.72413793 0.92 0.88461538 0.76923077 0.92307692 0.77419355 0.75 0.85714286 0.85185185 0.82142857] mean value: 0.8275677836767936 key: train_precision value: [0.96744186 0.96774194 0.97685185 0.98139535 0.97674419 0.97222222 0.97222222 0.97222222 0.98591549 0.96788991] mean value: 0.9740647250565854 key: test_recall value: [0.91304348 1. 1. 0.86956522 1. 1. 1. 1. 1. 1. ] mean value: 0.9782608695652174 key: train_recall value: [0.98578199 0.99526066 1. 1. 1. 1. 1. 1. 0.99526066 1. ] mean value: 0.9976303317535544 key: test_accuracy value: [0.78723404 0.95744681 0.93617021 0.80851064 0.95744681 0.85106383 0.82978723 0.91489362 0.91304348 0.89130435] mean value: 0.8846901017576319 key: train_accuracy value: [0.97624703 0.98099762 0.98812352 0.99049881 0.98812352 0.98574822 0.98574822 0.98574822 0.99052133 0.98341232] mean value: 0.98551688036834 key: test_roc_auc value: [0.78985507 0.95833333 0.9375 0.80978261 0.95652174 0.84782609 0.82608696 0.91304348 0.91304348 0.89130435] mean value: 0.8843297101449277 key: train_roc_auc value: [0.97622433 0.98096367 0.98809524 0.99047619 0.98815166 0.98578199 0.98578199 0.98578199 0.99052133 0.98341232] mean value: 0.9855190701873167 key: test_jcc value: [0.67741935 0.92 0.88461538 0.68965517 0.92307692 0.77419355 0.75 0.85714286 0.85185185 0.82142857] mean value: 0.8149383663755188 key: train_jcc value: [0.95412844 0.96330275 0.97685185 0.98139535 0.97674419 0.97222222 0.97222222 0.97222222 0.98130841 0.96788991] mean value: 0.9718287565534623 MCC on Blind test: -0.06 MCC on Training: 0.79 Running classifier: 2 Model_name: Bagging Classifier Model func: BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3))]) key: fit_time value: [0.23991728 0.22179437 0.20298409 0.23146605 0.22409368 0.25107956 0.2250905 0.22796392 0.21550679 0.27559471] mean value: 0.23154909610748292 key: score_time value: [0.08732724 0.05398965 0.0597837 0.07061553 0.07229519 0.07620263 0.05830932 0.05435538 0.07103705 0.05620313] mean value: 0.06601188182830811 key: test_mcc value: [0.95833333 1. 0.95833333 0.78804348 0.95825929 0.91804649 0.95825929 0.91804649 0.91651514 0.87705802] mean value: 0.9250894873386482 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.9787234 1. 0.9787234 0.89361702 0.97959184 0.96 0.97959184 0.96 0.95833333 0.93877551] mean value: 0.9627356346794038 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.95833333 1. 0.95833333 0.875 0.96 0.92307692 0.96 0.92307692 0.92 0.88461538] mean value: 0.9362435897435898 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 0.91304348 1. 1. 1. 1. 1. 1. ] mean value: 0.9913043478260869 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.9787234 1. 0.9787234 0.89361702 0.9787234 0.95744681 0.9787234 0.95744681 0.95652174 0.93478261] mean value: 0.9614708603145237 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.97916667 1. 0.97916667 0.89402174 0.97826087 0.95652174 0.97826087 0.95652174 0.95652174 0.93478261] mean value: 0.961322463768116 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.95833333 1. 0.95833333 0.80769231 0.96 0.92307692 0.96 0.92307692 0.92 0.88461538] mean value: 0.9295128205128206 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.05 MCC on Training: 0.93 Running classifier: 3 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.02282572 0.02224922 0.02262068 0.0209651 0.02378893 0.02419615 0.02375078 0.02348137 0.0230031 0.0242238 ] mean value: 0.023110485076904295 key: score_time value: [0.00977945 0.00932503 0.00916743 0.00944972 0.00969577 0.00994277 0.00962114 0.00906014 0.01028967 0.00929141] mean value: 0.009562253952026367 key: test_mcc value: [0.95833333 0.84254172 0.95833333 0.70289855 0.87917396 1. 0.76896316 0.76896316 0.83887049 0.83887049] mean value: 0.8556948211848934 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.9787234 0.92 0.9787234 0.85106383 0.94117647 1. 0.88888889 0.88888889 0.92 0.92 ] mean value: 0.9287464886663885 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.95833333 0.85185185 0.95833333 0.83333333 0.88888889 1. 0.8 0.8 0.85185185 0.85185185] mean value: 0.8794444444444445 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 0.86956522 1. 1. 1. 1. 1. 1. ] mean value: 0.9869565217391305 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.9787234 0.91489362 0.9787234 0.85106383 0.93617021 1. 0.87234043 0.87234043 0.91304348 0.91304348] mean value: 0.9230342275670674 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.97916667 0.91666667 0.97916667 0.85144928 0.93478261 1. 0.86956522 0.86956522 0.91304348 0.91304348] mean value: 0.9226449275362318 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.95833333 0.85185185 0.95833333 0.74074074 0.88888889 1. 0.8 0.8 0.85185185 0.85185185] mean value: 0.8701851851851853 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.12 MCC on Training: 0.86 Running classifier: 4 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.0102725 0.01048398 0.00970173 0.00969291 0.01062083 0.01055408 0.00971794 0.00984335 0.00991869 0.0099175 ] mean value: 0.01007235050201416 key: score_time value: [0.0091157 0.00879645 0.00864887 0.00876236 0.00996089 0.00958657 0.00874519 0.00866365 0.0087862 0.00908422] mean value: 0.00901501178741455 key: test_mcc value: [0.73692303 0.80641033 0.95833333 0.59613578 0.91804649 0.87917396 0.66534784 0.87917396 0.83887049 0.80178373] mean value: 0.808019895393788 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.86792453 0.90196078 0.9787234 0.80769231 0.96 0.94117647 0.84210526 0.94117647 0.92 0.90196078] mean value: 0.9062720013211332 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.76666667 0.82142857 0.95833333 0.72413793 0.92307692 0.88888889 0.72727273 0.88888889 0.85185185 0.82142857] mean value: 0.8371974353870906 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 0.91304348 1. 1. 1. 1. 1. 1. ] mean value: 0.9913043478260869 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.85106383 0.89361702 0.9787234 0.78723404 0.95744681 0.93617021 0.80851064 0.93617021 0.91304348 0.89130435] mean value: 0.8953283996299721 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.85416667 0.89583333 0.97916667 0.78985507 0.95652174 0.93478261 0.80434783 0.93478261 0.91304348 0.89130435] mean value: 0.8953804347826088 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.76666667 0.82142857 0.95833333 0.67741935 0.92307692 0.88888889 0.72727273 0.88888889 0.85185185 0.82142857] mean value: 0.8325255777675133 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.49 MCC on Training: 0.81 Running classifier: 5 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.12083626 0.12147236 0.1186235 0.11782002 0.11735272 0.12613845 0.11934257 0.1167419 0.11565852 0.11408162] mean value: 0.11880679130554199 key: score_time value: [0.01775599 0.0173099 0.01865268 0.01879334 0.01740456 0.01908636 0.0191102 0.01850963 0.01741886 0.01741409] mean value: 0.01814556121826172 key: test_mcc value: [1. 0.87979456 0.95833333 0.87318841 1. 0.87917396 0.87917396 0.84147165 1. 0.91651514] mean value: 0.922765101148084 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 0.93877551 0.9787234 0.93617021 1. 0.94117647 0.94117647 0.92307692 1. 0.95833333] mean value: 0.9617432324812085 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.88461538 0.95833333 0.91666667 1. 0.88888889 0.88888889 0.85714286 1. 0.92 ] mean value: 0.9314536019536019 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 0.95652174 1. 1. 1. 1. 1. 1. ] mean value: 0.9956521739130434 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 0.93617021 0.9787234 0.93617021 1. 0.93617021 0.93617021 0.91489362 1. 0.95652174] mean value: 0.9594819611470861 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 0.9375 0.97916667 0.9365942 1. 0.93478261 0.93478261 0.91304348 1. 0.95652174] mean value: 0.9592391304347826 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 0.88461538 0.95833333 0.88 1. 0.88888889 0.88888889 0.85714286 1. 0.92 ] mean value: 0.9277869352869352 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.05 MCC on Training: 0.92 Running classifier: 6 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.58115816 0.58122802 0.58239293 0.58648968 0.58248043 0.57449651 0.58559823 0.54803443 0.55124211 0.54945707] mean value: 0.5722577571868896 key: score_time value: [0.01057458 0.01031137 0.01068163 0.00982809 0.01006413 0.00921988 0.00927901 0.00902677 0.00906014 0.01014137] mean value: 0.009818696975708007 key: test_mcc value: [0.91833182 0.95833333 0.91833182 0.7085716 0.87917396 0.87917396 0.87917396 0.91804649 0.91651514 0.80178373] mean value: 0.8777435826465132 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.95833333 0.9787234 0.95833333 0.85714286 0.94117647 0.94117647 0.94117647 0.96 0.95833333 0.90196078] mean value: 0.9396356457476609 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.92 0.95833333 0.92 0.80769231 0.88888889 0.88888889 0.88888889 0.92307692 0.92 0.82142857] mean value: 0.8937197802197803 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 0.91304348 1. 1. 1. 1. 1. 1. ] mean value: 0.9913043478260869 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.95744681 0.9787234 0.95744681 0.85106383 0.93617021 0.93617021 0.93617021 0.95744681 0.95652174 0.89130435] mean value: 0.9358464384828864 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.95833333 0.97916667 0.95833333 0.85235507 0.93478261 0.93478261 0.93478261 0.95652174 0.95652174 0.89130435] mean value: 0.9356884057971016 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.92 0.95833333 0.92 0.75 0.88888889 0.88888889 0.88888889 0.92307692 0.92 0.82142857] mean value: 0.8879505494505494 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.22 MCC on Training: 0.88 Running classifier: 7 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.00974417 0.01058888 0.00944924 0.0094707 0.01002288 0.01045275 0.01077223 0.0096817 0.00942397 0.00986218] mean value: 0.009946870803833007 key: score_time value: [0.00871539 0.00954485 0.00894141 0.00909448 0.00865412 0.00895476 0.00946808 0.00899696 0.00932002 0.00910592] mean value: 0.009079599380493164 key: test_mcc value: [0.32108787 0.31876614 0.14889238 0.40398551 0.28051421 0.11685225 0.20543379 0.31876614 0.26726124 0.30905755] mean value: 0.2690617067856511 key: train_mcc value: [0.40147904 0.40994033 0.41027629 0.44115581 0.36341887 0.38103211 0.39851724 0.39820766 0.41710377 0.41811949] mean value: 0.4039250611293547 key: test_fscore value: [0.61904762 0.63636364 0.5 0.69565217 0.62222222 0.48780488 0.53658537 0.68 0.58536585 0.61904762] mean value: 0.5982089368155116 key: train_fscore value: [0.64850136 0.66842105 0.68193384 0.70792079 0.67942584 0.64893617 0.68170426 0.66492147 0.70644391 0.6977887 ] mean value: 0.678599739537119 key: test_precision value: [0.68421053 0.66666667 0.58823529 0.69565217 0.66666667 0.58823529 0.64705882 0.65384615 0.66666667 0.68421053] mean value: 0.6541448792155482 key: train_precision value: [0.76282051 0.75147929 0.73626374 0.74093264 0.68269231 0.73493976 0.71957672 0.73837209 0.71153846 0.7244898 ] mean value: 0.7303105318297382 key: test_recall value: [0.56521739 0.60869565 0.43478261 0.69565217 0.58333333 0.41666667 0.45833333 0.70833333 0.52173913 0.56521739] mean value: 0.5557971014492753 key: train_recall value: [0.56398104 0.60189573 0.63507109 0.67772512 0.67619048 0.58095238 0.64761905 0.6047619 0.7014218 0.67298578] mean value: 0.6362604378244188 key: test_accuracy value: [0.65957447 0.65957447 0.57446809 0.70212766 0.63829787 0.55319149 0.59574468 0.65957447 0.63043478 0.65217391] mean value: 0.6325161887141535 key: train_accuracy value: [0.6935867 0.70071259 0.70308789 0.71971496 0.68171021 0.68646081 0.69833729 0.695962 0.70853081 0.70853081] mean value: 0.6996634057930227 key: test_roc_auc value: [0.6576087 0.65851449 0.57155797 0.70199275 0.63949275 0.55615942 0.59873188 0.65851449 0.63043478 0.65217391] mean value: 0.6325181159420289 key: train_roc_auc value: [0.69389528 0.70094787 0.70324983 0.71981494 0.68169713 0.68621079 0.69821711 0.69574588 0.70853081 0.70853081] mean value: 0.6996840442338073 key: test_jcc value: [0.44827586 0.46666667 0.33333333 0.53333333 0.4516129 0.32258065 0.36666667 0.51515152 0.4137931 0.44827586] mean value: 0.4299689891124818 key: train_jcc value: [0.47983871 0.50197628 0.51737452 0.54789272 0.51449275 0.48031496 0.51711027 0.49803922 0.54612546 0.53584906] mean value: 0.5139013945900897 MCC on Blind test: -0.06 MCC on Training: 0.27 Running classifier: 8 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.1061945 0.13293076 0.12908816 0.12231207 0.12043643 0.13363528 0.11761117 0.11783624 0.14693952 0.17058539] mean value: 0.12975695133209228 key: score_time value: [0.03969431 0.02290606 0.02278209 0.02285242 0.02276063 0.02263355 0.02289534 0.02267194 0.02254915 0.02258825] mean value: 0.024433374404907227 key: test_mcc value: [0.82971014 0.68369322 0.87318841 0.59613578 0.91804649 0.75474102 0.87917396 0.8047833 0.75056834 0.80178373] mean value: 0.7891824388899108 key: train_mcc value: [0.92958367 0.93000284 0.93000284 0.92156936 0.92504284 0.93455374 0.93417209 0.93417209 0.92068326 0.94418894] mean value: 0.9303971669802369 key: test_fscore value: [0.91304348 0.84615385 0.93617021 0.80769231 0.96 0.88461538 0.94117647 0.90566038 0.88 0.90196078] mean value: 0.8976472861748818 key: train_fscore value: [0.96519722 0.96535797 0.96535797 0.9610984 0.9627907 0.96744186 0.96728972 0.96728972 0.96073903 0.97222222] mean value: 0.9654784798918659 key: test_precision value: [0.91304348 0.75862069 0.91666667 0.72413793 0.92307692 0.82142857 0.88888889 0.82758621 0.81481481 0.82142857] mean value: 0.8409692742151511 key: train_precision value: [0.94545455 0.94144144 0.94144144 0.92920354 0.94090909 0.94545455 0.94954128 0.94954128 0.93693694 0.95022624] mean value: 0.9430150354612241 key: test_recall value: [0.91304348 0.95652174 0.95652174 0.91304348 1. 0.95833333 1. 1. 0.95652174 1. ] mean value: 0.9653985507246376 key: train_recall value: [0.98578199 0.99052133 0.99052133 0.99526066 0.98571429 0.99047619 0.98571429 0.98571429 0.98578199 0.99526066] mean value: 0.9890747009704356 key: test_accuracy value: [0.91489362 0.82978723 0.93617021 0.78723404 0.95744681 0.87234043 0.93617021 0.89361702 0.86956522 0.89130435] mean value: 0.8888529139685477 key: train_accuracy value: [0.96437055 0.96437055 0.96437055 0.95961995 0.96199525 0.96674584 0.96674584 0.96674584 0.95971564 0.97156398] mean value: 0.96462439913994 key: test_roc_auc value: [0.91485507 0.83242754 0.9365942 0.78985507 0.95652174 0.87047101 0.93478261 0.89130435 0.86956522 0.89130435] mean value: 0.8887681159420291 key: train_roc_auc value: [0.96431957 0.96430828 0.96430828 0.95953509 0.96205146 0.96680208 0.96679079 0.96679079 0.95971564 0.97156398] mean value: 0.9646185962536673 key: test_jcc value: [0.84 0.73333333 0.88 0.67741935 0.92307692 0.79310345 0.88888889 0.82758621 0.78571429 0.82142857] mean value: 0.8170551012453127 key: train_jcc value: [0.93273543 0.93303571 0.93303571 0.92511013 0.92825112 0.93693694 0.93665158 0.93665158 0.92444444 0.94594595] mean value: 0.9332798602563364 MCC on Blind test: 0.05 MCC on Training: 0.79 Running classifier: 9 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.01248693 0.01065779 0.01096845 0.01008439 0.0094521 0.01010251 0.0101831 0.01078343 0.01138783 0.01191401] mean value: 0.010802054405212402 key: score_time value: [0.01921725 0.01902914 0.01776958 0.01839328 0.01746678 0.01786852 0.01847744 0.01853752 0.02020407 0.01886058] mean value: 0.018582415580749512 key: test_mcc value: [0.31884058 0.47117841 0.36231884 0.47117841 0.71722586 0.54211097 0.62966842 0.36699609 0.48566186 0.53452248] mean value: 0.4899701924629946 key: train_mcc value: [0.67002024 0.71305464 0.66711753 0.73417193 0.66938149 0.7159381 0.70770772 0.68965816 0.70677529 0.70407452] mean value: 0.6977899618998405 key: test_fscore value: [0.65217391 0.75471698 0.68085106 0.75471698 0.86792453 0.79245283 0.83018868 0.71698113 0.76 0.78431373] mean value: 0.7594319834438933 key: train_fscore value: [0.84415584 0.86324786 0.84301075 0.87234043 0.84322034 0.86393089 0.86026201 0.85217391 0.86021505 0.85900217] mean value: 0.8561559254873943 key: test_precision value: [0.65217391 0.66666667 0.66666667 0.66666667 0.79310345 0.72413793 0.75862069 0.65517241 0.7037037 0.71428571] mean value: 0.7001197813791518 key: train_precision value: [0.77689243 0.78599222 0.77165354 0.79150579 0.75954198 0.79051383 0.79435484 0.784 0.78740157 0.792 ] mean value: 0.7833856215228342 key: test_recall value: [0.65217391 0.86956522 0.69565217 0.86956522 0.95833333 0.875 0.91666667 0.79166667 0.82608696 0.86956522] mean value: 0.832427536231884 key: train_recall value: [0.92417062 0.95734597 0.92890995 0.97156398 0.94761905 0.95238095 0.93809524 0.93333333 0.9478673 0.93838863] mean value: 0.9439675016926202 key: test_accuracy value: [0.65957447 0.72340426 0.68085106 0.72340426 0.85106383 0.76595745 0.80851064 0.68085106 0.73913043 0.76086957] mean value: 0.7393617021276595 key: train_accuracy value: [0.82897862 0.847981 0.82660333 0.85748219 0.82422803 0.85035629 0.847981 0.83847981 0.84597156 0.84597156] mean value: 0.8414033389244746 key: test_roc_auc value: [0.65942029 0.72644928 0.68115942 0.72644928 0.84873188 0.76358696 0.80615942 0.67844203 0.73913043 0.76086957] mean value: 0.7390398550724637 key: train_roc_auc value: [0.82875197 0.8477206 0.82635974 0.85721056 0.82452042 0.85059806 0.84819454 0.83870458 0.84597156 0.84597156] mean value: 0.8414003610923043 key: test_jcc value: [0.48387097 0.60606061 0.51612903 0.60606061 0.76666667 0.65625 0.70967742 0.55882353 0.61290323 0.64516129] mean value: 0.6161603343683515 key: train_jcc value: [0.73033708 0.7593985 0.72862454 0.77358491 0.72893773 0.76045627 0.75478927 0.74242424 0.75471698 0.75285171] mean value: 0.7486121225184222 MCC on Blind test: -0.15 MCC on Training: 0.49 Running classifier: 10 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.04001975 0.07430935 0.0746417 0.04152036 0.07388234 0.05689502 0.07412338 0.07180071 0.05971909 0.0729475 ] mean value: 0.06398591995239258 key: score_time value: [0.01955056 0.02798986 0.01422358 0.01413059 0.02837324 0.01433873 0.02171707 0.01951241 0.02247548 0.0200386 ] mean value: 0.020235013961791993 key: test_mcc value: [0.61706091 0.63294907 0.50857834 0.4338259 0.79308818 0.60807084 0.54211097 0.62966842 0.76564149 0.66143783] mean value: 0.6192431951073629 key: train_mcc value: [0.8364296 0.82245344 0.82629859 0.80848438 0.8049295 0.84834683 0.82696095 0.81885282 0.80533735 0.81409191] mean value: 0.8212185360878619 key: test_fscore value: [0.8 0.82352941 0.76923077 0.74074074 0.90196078 0.82142857 0.79245283 0.83018868 0.88461538 0.83636364] mean value: 0.8200510807891496 key: train_fscore value: [0.92027335 0.91363636 0.91533181 0.90702948 0.90497738 0.9255079 0.91533181 0.91156463 0.90540541 0.90950226] mean value: 0.9128560376116412 key: test_precision value: [0.81818182 0.75 0.68965517 0.64516129 0.85185185 0.71875 0.72413793 0.75862069 0.79310345 0.71875 ] mean value: 0.7468212201735561 key: train_precision value: [0.88596491 0.87772926 0.88495575 0.86956522 0.86206897 0.87982833 0.88105727 0.87012987 0.86266094 0.87012987] mean value: 0.8744090384412031 key: test_recall value: [0.7826087 0.91304348 0.86956522 0.86956522 0.95833333 0.95833333 0.875 0.91666667 1. 1. ] mean value: 0.9143115942028984 key: train_recall value: [0.95734597 0.95260664 0.9478673 0.9478673 0.95238095 0.97619048 0.95238095 0.95714286 0.95260664 0.95260664] mean value: 0.9548995712028887 key: test_accuracy value: [0.80851064 0.80851064 0.74468085 0.70212766 0.89361702 0.78723404 0.76595745 0.80851064 0.86956522 0.80434783] mean value: 0.7993061979648474 key: train_accuracy value: [0.91686461 0.90973872 0.91211401 0.90261283 0.90023753 0.9216152 0.91211401 0.90736342 0.90047393 0.90521327] mean value: 0.9088347536333037 key: test_roc_auc value: [0.80797101 0.81068841 0.74728261 0.70561594 0.89221014 0.78351449 0.76358696 0.80615942 0.86956522 0.80434783] mean value: 0.7990942028985508 key: train_roc_auc value: [0.91676822 0.90963665 0.91202889 0.90250508 0.90036109 0.92174453 0.91220943 0.90748138 0.90047393 0.90521327] mean value: 0.9088422477995938 key: test_jcc value: [0.66666667 0.7 0.625 0.58823529 0.82142857 0.6969697 0.65625 0.70967742 0.79310345 0.71875 ] mean value: 0.6976081096813284 key: train_jcc value: [0.85232068 0.84100418 0.84388186 0.82987552 0.82644628 0.86134454 0.84388186 0.8375 0.82716049 0.8340249 ] mean value: 0.8397440299857853 MCC on Blind test: -0.31 MCC on Training: 0.62 Running classifier: 11 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.03950238 0.03888583 0.03968239 0.03996444 0.0696249 0.05942965 0.06014824 0.03932214 0.03851652 0.03821373] mean value: 0.04632902145385742 key: score_time value: [0.0123353 0.01240325 0.01233125 0.01229501 0.01232028 0.01235819 0.01264524 0.01239157 0.01239657 0.01260257] mean value: 0.012407922744750976 key: test_mcc value: [0.40398551 0.54621844 0.57713344 0.32123465 0.7023605 0.53483083 0.49454913 0.5326087 0.43519414 0.6092718 ] mean value: 0.515738712057158 key: train_mcc value: [0.68692709 0.71650468 0.71698624 0.71573889 0.67316739 0.64849128 0.68850673 0.72501257 0.63524947 0.7207759 ] mean value: 0.6927360230756031 key: test_fscore value: [0.69565217 0.78431373 0.79166667 0.66666667 0.85714286 0.78431373 0.76923077 0.76595745 0.71111111 0.80851064] mean value: 0.763456578081789 key: train_fscore value: [0.84651163 0.86238532 0.8630137 0.86111111 0.83990719 0.82464455 0.84862385 0.86448598 0.81967213 0.86247086] mean value: 0.8492826329225407 key: test_precision value: [0.69565217 0.71428571 0.76 0.64 0.84 0.74074074 0.71428571 0.7826087 0.72727273 0.79166667] mean value: 0.7406512432816781 key: train_precision value: [0.83105023 0.83555556 0.83259912 0.84162896 0.81900452 0.82075472 0.81858407 0.84862385 0.81018519 0.84862385] mean value: 0.830661006635648 key: test_recall value: [0.69565217 0.86956522 0.82608696 0.69565217 0.875 0.83333333 0.83333333 0.75 0.69565217 0.82608696] mean value: 0.7900362318840579 key: train_recall value: [0.86255924 0.89099526 0.8957346 0.88151659 0.86190476 0.82857143 0.88095238 0.88095238 0.82938389 0.87677725] mean value: 0.8689347777025501 key: test_accuracy value: [0.70212766 0.76595745 0.78723404 0.65957447 0.85106383 0.76595745 0.74468085 0.76595745 0.7173913 0.80434783] mean value: 0.7564292321924144 key: train_accuracy value: [0.8432304 0.85748219 0.85748219 0.85748219 0.83610451 0.82422803 0.8432304 0.86223278 0.81753555 0.86018957] mean value: 0.8459197802568923 key: test_roc_auc value: [0.70199275 0.76811594 0.78804348 0.66032609 0.85054348 0.76449275 0.74275362 0.76630435 0.7173913 0.80434783] mean value: 0.7564311594202898 key: train_roc_auc value: [0.84318438 0.85740239 0.85739111 0.85742496 0.83616565 0.82423832 0.84331979 0.86227714 0.81753555 0.86018957] mean value: 0.8459128864816069 key: test_jcc value: [0.53333333 0.64516129 0.65517241 0.5 0.75 0.64516129 0.625 0.62068966 0.55172414 0.67857143] mean value: 0.6204813549446475 key: train_jcc value: [0.73387097 0.75806452 0.75903614 0.75609756 0.724 0.7016129 0.73705179 0.76131687 0.69444444 0.75819672] mean value: 0.7383691923663286 MCC on Blind test: -0.1 MCC on Training: 0.52 Running classifier: 12 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(cv=3, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(cv=3, random_state=42))]) key: fit_time value: [0.53623366 0.65826464 0.54399109 0.51737332 0.50376439 0.5685761 0.51915765 0.50869083 0.5110805 0.53574371] mean value: 0.5402875900268554 key: score_time value: [0.01224351 0.01240373 0.01224518 0.01208639 0.01221728 0.01228213 0.01247406 0.01235342 0.0121336 0.01213837] mean value: 0.012257766723632813 key: test_mcc value: [0.7070024 0.68369322 0.66243303 0.49819858 1. 0.79308818 0.75474102 0.7070024 0.76564149 0.66143783] mean value: 0.7233238146155193 key: train_mcc value: [0.90514675 0.89584331 0.90998989 0.90998989 0.9102487 0.95777743 0.91975444 0.91553193 0.92521027 0.93017484] mean value: 0.9179667462334102 key: test_fscore value: [0.8372093 0.84615385 0.83333333 0.76 1. 0.90196078 0.88461538 0.8627451 0.88461538 0.83636364] mean value: 0.8646996769760108 key: train_fscore value: [0.95305164 0.94859813 0.95550351 0.95550351 0.95550351 0.97892272 0.96018735 0.95813953 0.96296296 0.96535797] mean value: 0.9593730848447029 key: test_precision value: [0.9 0.75862069 0.8 0.7037037 1. 0.85185185 0.82142857 0.81481481 0.79310345 0.71875 ] mean value: 0.8162273079729976 key: train_precision value: [0.94418605 0.93548387 0.94444444 0.94444444 0.94009217 0.96313364 0.94470046 0.93636364 0.94117647 0.94144144] mean value: 0.9435466622042676 key: test_recall value: [0.7826087 0.95652174 0.86956522 0.82608696 1. 0.95833333 0.95833333 0.91666667 1. 1. ] mean value: 0.9268115942028986 key: train_recall value: [0.96208531 0.96208531 0.96682464 0.96682464 0.97142857 0.9952381 0.97619048 0.98095238 0.98578199 0.99052133] mean value: 0.9757932746558339 key: test_accuracy value: [0.85106383 0.82978723 0.82978723 0.74468085 1. 0.89361702 0.87234043 0.85106383 0.86956522 0.80434783] mean value: 0.8546253469010177 key: train_accuracy value: [0.95249406 0.94774347 0.95486936 0.95486936 0.95486936 0.97862233 0.95961995 0.95724466 0.96208531 0.96445498] mean value: 0.9586872825927886 key: test_roc_auc value: [0.84963768 0.83242754 0.83061594 0.74637681 1. 0.89221014 0.87047101 0.84963768 0.86956522 0.80434783] mean value: 0.8545289855072464 key: train_roc_auc value: [0.95247123 0.94770932 0.95484089 0.95484089 0.9549086 0.9786617 0.95965922 0.95730084 0.96208531 0.96445498] mean value: 0.958693297224103 key: test_jcc value: [0.72 0.73333333 0.71428571 0.61290323 1. 0.82142857 0.79310345 0.75862069 0.79310345 0.71875 ] mean value: 0.7665528431060967 key: train_jcc value: [0.9103139 0.90222222 0.91479821 0.91479821 0.91479821 0.9587156 0.92342342 0.91964286 0.92857143 0.93303571] mean value: 0.9220319762155291 MCC on Blind test: 0.03 MCC on Training: 0.72 Running classifier: 13 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [2.00788093 2.11751461 1.98341274 1.61928368 1.76066446 2.11657715 1.37238121 2.06990314 1.95610619 1.41096473] mean value: 1.84146888256073 key: score_time value: [0.01232338 0.01341581 0.01230669 0.01852036 0.01310587 0.01292801 0.01293135 0.01401639 0.0125165 0.01239109] mean value: 0.013445544242858886 key: test_mcc value: [0.74773263 0.77125066 0.7876601 0.55975995 0.83243502 0.84147165 0.8047833 0.8047833 0.91651514 0.80178373] mean value: 0.7868175472446481 key: train_mcc value: [0.96675619 0.9857591 0.97154077 0.96701665 0.95253786 0.98117552 0.90974949 0.98104223 0.98579306 0.94418894] mean value: 0.9645559817901506 key: test_fscore value: [0.875 0.88461538 0.88888889 0.79245283 0.92 0.92307692 0.90566038 0.90566038 0.95833333 0.90196078] mean value: 0.8955648899133916 key: train_fscore value: [0.98345154 0.9929078 0.98571429 0.98360656 0.97630332 0.99056604 0.95486936 0.99052133 0.9929078 0.97222222] mean value: 0.9823070245748907 key: test_precision value: [0.84 0.79310345 0.90909091 0.7 0.88461538 0.85714286 0.82758621 0.82758621 0.92 0.82142857] mean value: 0.8380553584346687 key: train_precision value: [0.98113208 0.99056604 0.99043062 0.97222222 0.97169811 0.98130841 0.95260664 0.98584906 0.99056604 0.95022624] mean value: 0.9766605455616443 key: test_recall value: [0.91304348 1. 0.86956522 0.91304348 0.95833333 1. 1. 1. 1. 1. ] mean value: 0.9653985507246376 key: train_recall value: [0.98578199 0.99526066 0.98104265 0.99526066 0.98095238 1. 0.95714286 0.9952381 0.99526066 0.99526066] mean value: 0.9881200631911533 key: test_accuracy value: [0.87234043 0.87234043 0.89361702 0.76595745 0.91489362 0.91489362 0.89361702 0.89361702 0.95652174 0.89130435] mean value: 0.8869102682701202 key: train_accuracy value: [0.98337292 0.99287411 0.98574822 0.98337292 0.97624703 0.99049881 0.95486936 0.99049881 0.992891 0.97156398] mean value: 0.9821937161576478 key: test_roc_auc value: [0.87318841 0.875 0.89311594 0.76902174 0.91394928 0.91304348 0.89130435 0.89130435 0.95652174 0.89130435] mean value: 0.8867753623188406 key: train_roc_auc value: [0.98336719 0.99286843 0.98575942 0.98334462 0.97625818 0.99052133 0.95487475 0.99051004 0.992891 0.97156398] mean value: 0.9821958925750394 key: test_jcc value: [0.77777778 0.79310345 0.8 0.65625 0.85185185 0.85714286 0.82758621 0.82758621 0.92 0.82142857] mean value: 0.8132726920270024 key: train_jcc value: [0.96744186 0.98591549 0.97183099 0.96774194 0.9537037 0.98130841 0.91363636 0.98122066 0.98591549 0.94594595] mean value: 0.9654660849557934 MCC on Blind test: -0.06 MCC on Training: 0.79 Running classifier: 14 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.0133822 0.01309013 0.00982285 0.00978351 0.01016593 0.00924134 0.00978088 0.0103178 0.00917459 0.00913453] mean value: 0.010389375686645507 key: score_time value: [0.01159763 0.01024103 0.0088017 0.00855303 0.00847244 0.00918913 0.00917935 0.00852633 0.0084157 0.00847459] mean value: 0.009145092964172364 key: test_mcc value: [0.27657348 0.50321854 0.40437762 0.31884058 0.02367977 0.31876614 0.01676632 0.31884058 0.26726124 0.31526414] mean value: 0.276358841859594 key: train_mcc value: [0.43979504 0.38467826 0.41333175 0.46063437 0.26392309 0.35537818 0.31633256 0.3586775 0.32230746 0.40861678] mean value: 0.3723674977493635 key: test_fscore value: [0.60465116 0.7 0.68181818 0.65217391 0.48888889 0.68 0.56603774 0.66666667 0.58536585 0.6 ] mean value: 0.6225602402715507 key: train_fscore value: [0.71497585 0.675 0.69 0.71641791 0.62102689 0.66 0.64705882 0.67933492 0.65871122 0.69287469] mean value: 0.67554003011764 key: test_precision value: [0.65 0.82352941 0.71428571 0.65217391 0.52380952 0.65384615 0.51724138 0.66666667 0.66666667 0.70588235] mean value: 0.657410178233443 key: train_precision value: [0.72906404 0.71428571 0.73015873 0.7539267 0.63819095 0.69473684 0.66666667 0.67772512 0.66346154 0.71938776] mean value: 0.6987604061016783 key: test_recall value: [0.56521739 0.60869565 0.65217391 0.65217391 0.45833333 0.70833333 0.625 0.66666667 0.52173913 0.52173913] mean value: 0.5980072463768116 key: train_recall value: [0.7014218 0.63981043 0.65402844 0.68246445 0.6047619 0.62857143 0.62857143 0.68095238 0.65402844 0.66824645] mean value: 0.6542857142857142 key: test_accuracy value: [0.63829787 0.74468085 0.70212766 0.65957447 0.5106383 0.65957447 0.5106383 0.65957447 0.63043478 0.65217391] mean value: 0.6367715078630897 key: train_accuracy value: [0.71971496 0.6912114 0.70546318 0.72921615 0.63182898 0.67695962 0.65795724 0.67933492 0.66113744 0.70379147] mean value: 0.6856615370760208 key: test_roc_auc value: [0.63677536 0.74184783 0.70108696 0.65942029 0.51177536 0.65851449 0.50815217 0.65942029 0.63043478 0.65217391] mean value: 0.6359601449275362 key: train_roc_auc value: [0.71975852 0.69133378 0.70558565 0.72932747 0.63176484 0.67684496 0.65788761 0.67933875 0.66113744 0.70379147] mean value: 0.685677048070413 key: test_jcc value: [0.43333333 0.53846154 0.51724138 0.48387097 0.32352941 0.51515152 0.39473684 0.5 0.4137931 0.42857143] mean value: 0.4548689519888341 key: train_jcc value: [0.55639098 0.50943396 0.52671756 0.55813953 0.45035461 0.49253731 0.47826087 0.51438849 0.4911032 0.53007519] mean value: 0.5107401704796053 MCC on Blind test: -0.02 MCC on Training: 0.28 Running classifier: 15 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.01153851 0.01105261 0.0102303 0.01109815 0.01012635 0.01082087 0.01015401 0.01118016 0.01192546 0.01167822] mean value: 0.010980463027954102 key: score_time value: [0.00974751 0.00992513 0.00966859 0.00982356 0.00912738 0.00897431 0.00951076 0.00895786 0.01022696 0.01021481] mean value: 0.00961768627166748 key: test_mcc value: [0.49454913 0.38613937 0.11506227 0.25103889 0.28051421 0.20543379 0.34400292 0.38259687 0.27386128 0.19802951] mean value: 0.2931228229786954 key: train_mcc value: [0.41339684 0.40891403 0.46148719 0.45893404 0.39087773 0.41976306 0.43325016 0.44695709 0.451632 0.51772034] mean value: 0.44029324798503044 key: test_fscore value: [0.71428571 0.59459459 0.36363636 0.5 0.62222222 0.53658537 0.6 0.63414634 0.56410256 0.45714286] mean value: 0.5586716023301389 key: train_fscore value: [0.61127596 0.59818731 0.65317919 0.66478873 0.64109589 0.62790698 0.60307692 0.64942529 0.67574932 0.71698113] mean value: 0.6441666727180702 key: test_precision value: [0.78947368 0.78571429 0.6 0.69230769 0.66666667 0.64705882 0.75 0.76470588 0.6875 0.66666667] mean value: 0.705009370144819 key: train_precision value: [0.81746032 0.825 0.83703704 0.81944444 0.75483871 0.80597015 0.85217391 0.81884058 0.79487179 0.83125 ] mean value: 0.8156886945498367 key: test_recall value: [0.65217391 0.47826087 0.26086957 0.39130435 0.58333333 0.45833333 0.5 0.54166667 0.47826087 0.34782609] mean value: 0.4692028985507246 key: train_recall value: [0.48815166 0.46919431 0.53554502 0.55924171 0.55714286 0.51428571 0.46666667 0.53809524 0.58767773 0.63033175] mean value: 0.5346332656285263 key: test_accuracy value: [0.74468085 0.68085106 0.55319149 0.61702128 0.63829787 0.59574468 0.65957447 0.68085106 0.63043478 0.58695652] mean value: 0.6387604070305273 key: train_accuracy value: [0.6888361 0.68408551 0.71496437 0.71733967 0.6888361 0.695962 0.6935867 0.71021378 0.71800948 0.75118483] mean value: 0.7063018540824713 key: test_roc_auc value: [0.74275362 0.67663043 0.54710145 0.61231884 0.63949275 0.59873188 0.66304348 0.68387681 0.63043478 0.58695652] mean value: 0.6381340579710144 key: train_roc_auc value: [0.68931392 0.68459716 0.71539156 0.71771609 0.68852404 0.69553148 0.69304897 0.70980591 0.71800948 0.75118483] mean value: 0.7063123448431506 key: test_jcc value: [0.55555556 0.42307692 0.22222222 0.33333333 0.4516129 0.36666667 0.42857143 0.46428571 0.39285714 0.2962963 ] mean value: 0.39344781860910893 key: train_jcc value: [0.44017094 0.42672414 0.48497854 0.4978903 0.47177419 0.45762712 0.43171806 0.48085106 0.51028807 0.55882353] mean value: 0.47608459471847936 MCC on Blind test: -0.06 MCC on Training: 0.29 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=12, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.01630116 0.01988673 0.02037549 0.02328515 0.02284193 0.02449727 0.02401662 0.01988435 0.02269149 0.02141166] mean value: 0.021519184112548828 key: score_time value: [0.0090301 0.01182556 0.01188469 0.01196694 0.01192927 0.01204348 0.01197767 0.0118444 0.01183438 0.01187301] mean value: 0.01162095069885254 key: test_mcc value: [0.57427536 0.18822754 0.3779873 0.30777746 0.64404991 0.55975995 0.4078185 0.30093209 0.43852901 0.13093073] mean value: 0.39302878555532994 key: train_mcc value: [0.73794258 0.35582491 0.58197617 0.30704718 0.61049771 0.60187447 0.7108915 0.42106118 0.69685173 0.55661265] mean value: 0.558058006854668 key: test_fscore value: [0.7826087 0.33333333 0.72413793 0.4 0.83636364 0.73170732 0.73076923 0.47058824 0.69767442 0.58333333] mean value: 0.629051613145813 key: train_fscore value: [0.87387387 0.38167939 0.80384615 0.31075697 0.81584158 0.72727273 0.85780886 0.5034965 0.83627204 0.75578406] mean value: 0.6866632163879989 key: test_precision value: [0.7826087 0.71428571 0.6 0.85714286 0.74193548 0.88235294 0.67857143 0.8 0.75 0.56 ] mean value: 0.7366897120699611 key: train_precision value: [0.83261803 0.98039216 0.6763754 0.975 0.69830508 0.94656489 0.84018265 0.94736842 0.89247312 0.8258427 ] mean value: 0.861512244174975 key: test_recall value: [0.7826087 0.2173913 0.91304348 0.26086957 0.95833333 0.625 0.79166667 0.33333333 0.65217391 0.60869565] mean value: 0.6143115942028985 key: train_recall value: [0.91943128 0.23696682 0.99052133 0.18483412 0.98095238 0.59047619 0.87619048 0.34285714 0.78672986 0.69668246] mean value: 0.6605642067253441 key: test_accuracy value: [0.78723404 0.57446809 0.65957447 0.61702128 0.80851064 0.76595745 0.70212766 0.61702128 0.7173913 0.56521739] mean value: 0.6814523589269195 key: train_accuracy value: [0.86698337 0.6152019 0.75771971 0.58907363 0.77909739 0.77909739 0.85510689 0.66270784 0.84597156 0.77488152] mean value: 0.7525841204084159 key: test_roc_auc value: [0.78713768 0.56702899 0.66485507 0.60960145 0.80525362 0.76902174 0.70018116 0.62318841 0.7173913 0.56521739] mean value: 0.6808876811594202 key: train_roc_auc value: [0.8668585 0.61610246 0.75716543 0.59003611 0.77957572 0.77865042 0.85515685 0.6619499 0.84597156 0.77488152] mean value: 0.7526348454073573 key: test_jcc value: [0.64285714 0.2 0.56756757 0.25 0.71875 0.57692308 0.57575758 0.30769231 0.53571429 0.41176471] mean value: 0.47870266623943103 key: train_jcc value: [0.776 0.23584906 0.67202572 0.18396226 0.68896321 0.57142857 0.75102041 0.3364486 0.71861472 0.60743802] mean value: 0.5541750567796049 MCC on Blind test: -0.15 MCC on Training: 0.39 Running classifier: 17 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.02495122 0.02813077 0.02878737 0.02748108 0.02891684 0.02861595 0.0321517 0.02858019 0.02719641 0.02871466] mean value: 0.02835261821746826 key: score_time value: [0.01232648 0.01256442 0.01249456 0.0132587 0.01249886 0.01240015 0.01249361 0.01325941 0.01245356 0.01248765] mean value: 0.012623739242553712 key: test_mcc value: [1. 0.87979456 1. 0.87917396 1. 1. 1. 0.95825929 1. 1. ] mean value: 0.9717227812371643 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 0.93877551 1. 0.93023256 1. 1. 1. 0.97959184 1. 1. ] mean value: 0.9848599905078309 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.88461538 1. 1. 1. 1. 1. 0.96 1. 1. ] mean value: 0.9844615384615384 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 0.86956522 1. 1. 1. 1. 1. 1. ] mean value: 0.9869565217391305 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 0.93617021 1. 0.93617021 1. 1. 1. 0.9787234 1. 1. ] mean value: 0.9851063829787234 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 0.9375 1. 0.93478261 1. 1. 1. 0.97826087 1. 1. ] mean value: 0.9850543478260869 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 0.88461538 1. 0.86956522 1. 1. 1. 0.96 1. 1. ] mean value: 0.9714180602006689 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.0 MCC on Training: 0.97 Running classifier: 18 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42))]) key: fit_time value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( [0.68014336 0.65044999 0.65829301 0.61055255 0.68748403 0.65241504 0.64393568 0.65714049 0.6974566 0.66003919] mean value: 0.6597909927368164 key: score_time value: [0.17867732 0.17685342 0.14835882 0.16395378 0.16278124 0.20602441 0.24666882 0.17792821 0.23104024 0.16945291] mean value: 0.1861739158630371 key: test_mcc value: [1. 0.91833182 0.91833182 0.82971014 1. 0.87917396 0.84147165 0.84147165 0.91651514 0.83887049] mean value: 0.8983876678846082 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 0.95833333 0.95833333 0.91304348 1. 0.94117647 0.92307692 0.92307692 0.95833333 0.92 ] mean value: 0.9495373795002952 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.92 0.92 0.91304348 1. 0.88888889 0.85714286 0.85714286 0.92 0.85185185] mean value: 0.9128069933287323 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 0.91304348 1. 1. 1. 1. 1. 1. ] mean value: 0.9913043478260869 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 0.95744681 0.95744681 0.91489362 1. 0.93617021 0.91489362 0.91489362 0.95652174 0.91304348] mean value: 0.9465309898242369 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 0.95833333 0.95833333 0.91485507 1. 0.93478261 0.91304348 0.91304348 0.95652174 0.91304348] mean value: 0.9461956521739131 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 0.92 0.92 0.84 1. 0.88888889 0.85714286 0.85714286 0.92 0.85185185] mean value: 0.9055026455026454 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.05 MCC on Training: 0.9 Running classifier: 19 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea...age_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42))]) key: fit_time value: [1.00568151 0.95360398 0.97229195 1.03924489 0.96792197 0.98790073 0.98077679 0.98440409 1.00906062 0.98692203] mean value: 0.9887808561325073 key: score_time value: [0.24086666 0.24266195 0.21584535 0.23906255 0.20887041 0.23870254 0.26695418 0.22554517 0.23276401 0.2230773 ] mean value: 0.23343501091003419 key: test_mcc value: [0.74682354 0.87318841 0.87318841 0.70289855 0.95825929 0.79308818 0.84147165 0.7070024 0.87038828 0.83887049] mean value: 0.8205179187672863 key: train_mcc value: [0.95725473 0.97153949 0.97625765 0.97625765 0.97634685 0.97154077 0.98104223 0.95725473 0.97160763 0.96709621] mean value: 0.9706197939045994 key: test_fscore value: [0.86363636 0.93617021 0.93617021 0.85106383 0.97959184 0.90196078 0.92307692 0.8627451 0.93617021 0.92 ] mean value: 0.9110585473886028 key: train_fscore value: [0.9787234 0.98584906 0.98817967 0.98817967 0.98817967 0.98578199 0.99052133 0.97852029 0.98584906 0.98360656] mean value: 0.985339068586384 key: test_precision value: [0.9047619 0.91666667 0.91666667 0.83333333 0.96 0.85185185 0.85714286 0.81481481 0.91666667 0.85185185] mean value: 0.8823756613756613 key: train_precision value: [0.97641509 0.98122066 0.98584906 0.98584906 0.98122066 0.98113208 0.98584906 0.98086124 0.98122066 0.97222222] mean value: 0.9811839777694988 key: test_recall value: [0.82608696 0.95652174 0.95652174 0.86956522 1. 0.95833333 1. 0.91666667 0.95652174 1. ] mean value: 0.9440217391304347 key: train_recall value: [0.98104265 0.99052133 0.99052133 0.99052133 0.9952381 0.99047619 0.9952381 0.97619048 0.99052133 0.99526066] mean value: 0.9895531482735274 key: test_accuracy value: [0.87234043 0.93617021 0.93617021 0.85106383 0.9787234 0.89361702 0.91489362 0.85106383 0.93478261 0.91304348] mean value: 0.9081868640148011 key: train_accuracy value: [0.97862233 0.98574822 0.98812352 0.98812352 0.98812352 0.98574822 0.99049881 0.97862233 0.98578199 0.98341232] mean value: 0.9852804764102622 key: test_roc_auc value: [0.87137681 0.9365942 0.9365942 0.85144928 0.97826087 0.89221014 0.91304348 0.84963768 0.93478261 0.91304348] mean value: 0.9076992753623188 key: train_roc_auc value: [0.97861657 0.98573685 0.98811781 0.98811781 0.98814037 0.98575942 0.99051004 0.97861657 0.98578199 0.98341232] mean value: 0.9852809749492215 key: test_jcc value: [0.76 0.88 0.88 0.74074074 0.96 0.82142857 0.85714286 0.75862069 0.88 0.85185185] mean value: 0.8389784710819193 key: train_jcc value: [0.95833333 0.97209302 0.97663551 0.97663551 0.97663551 0.97196262 0.98122066 0.95794393 0.97209302 0.96774194] mean value: 0.9711295056717976 MCC on Blind test: -0.06 MCC on Training: 0.82 Running classifier: 20 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.02649355 0.01462197 0.01508951 0.03602529 0.03615999 0.03728056 0.03587484 0.06211948 0.03846216 0.04948974] mean value: 0.035161709785461424 key: score_time value: [0.01186156 0.01178002 0.01200223 0.02268481 0.02168226 0.02354932 0.02531981 0.02436423 0.02234983 0.02455378] mean value: 0.02001478672027588 key: test_mcc value: [0.66243303 0.50857834 0.57713344 0.45173716 0.74682354 0.54211097 0.58127976 0.62091661 0.70164642 0.52623481] mean value: 0.5918894075979229 key: train_mcc value: [0.78370001 0.79452148 0.7591323 0.78497489 0.76531927 0.75353432 0.77716823 0.75843635 0.78552728 0.75477613] mean value: 0.771709024464412 key: test_fscore value: [0.83333333 0.76923077 0.79166667 0.73469388 0.88 0.79245283 0.80769231 0.82352941 0.85714286 0.7755102 ] mean value: 0.8065252257651971 key: train_fscore value: [0.89497717 0.90045249 0.88275862 0.8959276 0.88584475 0.87850467 0.88992974 0.88111888 0.8959276 0.88018433] mean value: 0.8885625859007638 key: test_precision value: [0.8 0.68965517 0.76 0.69230769 0.84615385 0.72413793 0.75 0.77777778 0.80769231 0.73076923] mean value: 0.7578493958149131 key: train_precision value: [0.86343612 0.86147186 0.85714286 0.85714286 0.85087719 0.86238532 0.87557604 0.8630137 0.85714286 0.85650224] mean value: 0.8604691047980786 key: test_recall value: [0.86956522 0.86956522 0.82608696 0.7826087 0.91666667 0.875 0.875 0.875 0.91304348 0.82608696] mean value: 0.8628623188405795 key: train_recall value: [0.92890995 0.94312796 0.90995261 0.93838863 0.92380952 0.8952381 0.9047619 0.9 0.93838863 0.90521327] mean value: 0.9187790566463553 key: test_accuracy value: [0.82978723 0.74468085 0.78723404 0.72340426 0.87234043 0.76595745 0.78723404 0.80851064 0.84782609 0.76086957] mean value: 0.7927844588344126 key: train_accuracy value: [0.89073634 0.89548694 0.87885986 0.89073634 0.88123515 0.87648456 0.88836105 0.87885986 0.89099526 0.87677725] mean value: 0.8848532606860218 key: test_roc_auc value: [0.83061594 0.74728261 0.78804348 0.72463768 0.87137681 0.76358696 0.78532609 0.80706522 0.84782609 0.76086957] mean value: 0.7926630434782609 key: train_roc_auc value: [0.89064545 0.8953735 0.87878583 0.89062288 0.88133604 0.876529 0.88839991 0.87890995 0.89099526 0.87677725] mean value: 0.8848375084631008 key: test_jcc value: [0.71428571 0.625 0.65517241 0.58064516 0.78571429 0.65625 0.67741935 0.7 0.75 0.63333333] mean value: 0.677782026325547 key: train_jcc value: [0.80991736 0.81893004 0.79012346 0.81147541 0.79508197 0.78333333 0.80168776 0.7875 0.81147541 0.78600823] mean value: 0.7995532967698621 MCC on Blind test: -0.02 MCC on Training: 0.59 Running classifier: 21 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=3))]) key: fit_time value: [0.14179134 0.11882591 0.16219544 0.09933257 0.11554313 0.11672735 0.11317706 0.06691337 0.11591411 0.11581326] mean value: 0.11662335395812988 key: score_time value: [0.0241673 0.02513003 0.02583432 0.0221765 0.01649475 0.02139068 0.0224514 0.02109408 0.02285075 0.02124071] mean value: 0.022283053398132323 key: test_mcc value: [0.57427536 0.59613578 0.62296012 0.59613578 0.74682354 0.60807084 0.68038162 0.7070024 0.80178373 0.69560834] mean value: 0.6629177509651635 key: train_mcc value: [0.81291532 0.81291532 0.82182216 0.80848438 0.76531927 0.80856539 0.8320588 0.8320588 0.80457036 0.82733417] mean value: 0.8126043968916148 key: test_fscore value: [0.7826087 0.80769231 0.81632653 0.80769231 0.88 0.82142857 0.85185185 0.8627451 0.90196078 0.85185185] mean value: 0.838415799913425 key: train_fscore value: [0.90909091 0.90909091 0.91324201 0.90702948 0.88584475 0.90660592 0.91780822 0.91780822 0.90497738 0.91571754] mean value: 0.9087215330967091 key: test_precision value: [0.7826087 0.72413793 0.76923077 0.72413793 0.84615385 0.71875 0.76666667 0.81481481 0.82142857 0.74193548] mean value: 0.7709864709886776 key: train_precision value: [0.87336245 0.87336245 0.88105727 0.86956522 0.85087719 0.86899563 0.88157895 0.88157895 0.86580087 0.88157895] mean value: 0.8727757911019823 key: test_recall value: [0.7826087 0.91304348 0.86956522 0.91304348 0.91666667 0.95833333 0.95833333 0.91666667 1. 1. ] mean value: 0.9228260869565217 key: train_recall value: [0.9478673 0.9478673 0.9478673 0.9478673 0.92380952 0.94761905 0.95714286 0.95714286 0.9478673 0.95260664] mean value: 0.947765741367637 key: test_accuracy value: [0.78723404 0.78723404 0.80851064 0.78723404 0.87234043 0.78723404 0.82978723 0.85106383 0.89130435 0.82608696] mean value: 0.8228029602220166 key: train_accuracy value: [0.90498812 0.90498812 0.90973872 0.90261283 0.88123515 0.90261283 0.91448931 0.91448931 0.90047393 0.91232227] mean value: 0.9047950602830092 key: test_roc_auc value: [0.78713768 0.78985507 0.80978261 0.78985507 0.87137681 0.78351449 0.82699275 0.84963768 0.89130435 0.82608696] mean value: 0.8225543478260869 key: train_roc_auc value: [0.90488603 0.90488603 0.90964794 0.90250508 0.88133604 0.90271948 0.91459039 0.91459039 0.90047393 0.91232227] mean value: 0.9047957571654253 key: test_jcc value: [0.64285714 0.67741935 0.68965517 0.67741935 0.78571429 0.6969697 0.74193548 0.75862069 0.82142857 0.74193548] mean value: 0.7233955236458017 key: train_jcc value: [0.83333333 0.83333333 0.84033613 0.82987552 0.79508197 0.82916667 0.84810127 0.84810127 0.82644628 0.84453782] mean value: 0.8328313581435784 MCC on Blind test: -0.1 MCC on Training: 0.66 Running classifier: 22 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.03672028 0.02366519 0.02284932 0.02281833 0.02205729 0.02238941 0.02056789 0.02344179 0.02322078 0.02307701] mean value: 0.024080729484558104 key: score_time value: [0.01394796 0.01359487 0.01342154 0.01635337 0.01331306 0.01278925 0.01321697 0.0131073 0.01279378 0.01319575] mean value: 0.013573384284973145 key: test_mcc value: [0.49183384 0.62296012 0.65942029 0.36231884 0.82971014 0.71722586 0.66801039 0.61706091 0.48007936 0.48007936] mean value: 0.5928699107152616 key: train_mcc value: [0.71083285 0.71650468 0.706571 0.73661034 0.70306396 0.71042891 0.7161657 0.73614211 0.68376346 0.71576844] mean value: 0.7135851458319136 key: test_fscore value: [0.75 0.81632653 0.82608696 0.68085106 0.91666667 0.86792453 0.84615385 0.81632653 0.72727273 0.72727273] mean value: 0.7974881577243871 key: train_fscore value: [0.85846868 0.86238532 0.85714286 0.87272727 0.85583524 0.85647059 0.86111111 0.87155963 0.84597701 0.85915493] mean value: 0.8600832642185493 key: test_precision value: [0.72 0.76923077 0.82608696 0.66666667 0.91666667 0.79310345 0.78571429 0.8 0.76190476 0.76190476] mean value: 0.7801278316885514 key: train_precision value: [0.84090909 0.83555556 0.83408072 0.83842795 0.82378855 0.84651163 0.83783784 0.84070796 0.82142857 0.85116279] mean value: 0.8370410650280025 key: test_recall value: [0.7826087 0.86956522 0.82608696 0.69565217 0.91666667 0.95833333 0.91666667 0.83333333 0.69565217 0.69565217] mean value: 0.8190217391304347 key: train_recall value: [0.87677725 0.89099526 0.88151659 0.90995261 0.89047619 0.86666667 0.88571429 0.9047619 0.87203791 0.86729858] mean value: 0.8846197246671181 key: test_accuracy value: [0.74468085 0.80851064 0.82978723 0.68085106 0.91489362 0.85106383 0.82978723 0.80851064 0.73913043 0.73913043] mean value: 0.7946345975948195 key: train_accuracy value: [0.85510689 0.85748219 0.85273159 0.86698337 0.85035629 0.85510689 0.85748219 0.86698337 0.84123223 0.85781991] mean value: 0.856128491179881 key: test_roc_auc value: [0.74547101 0.80978261 0.82971014 0.68115942 0.91485507 0.84873188 0.82789855 0.80797101 0.73913043 0.73913043] mean value: 0.7943840579710144 key: train_roc_auc value: [0.85505529 0.85740239 0.85266306 0.86688107 0.85045137 0.85513428 0.85754909 0.8670729 0.84123223 0.85781991] mean value: 0.8561261566237869 key: test_jcc value: [0.6 0.68965517 0.7037037 0.51612903 0.84615385 0.76666667 0.73333333 0.68965517 0.57142857 0.57142857] mean value: 0.6688154069800343 key: train_jcc value: [0.75203252 0.75806452 0.75 0.77419355 0.748 0.74897119 0.75609756 0.77235772 0.73306773 0.75308642] mean value: 0.7545871211646566 MCC on Blind test: -0.1 MCC on Training: 0.59 Running classifier: 23 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.01905584 0.01959229 0.02303696 0.02095652 0.0199883 0.02288318 0.0209682 0.02249193 0.02734208 0.02007413] mean value: 0.021638941764831544 key: score_time value: [0.00951004 0.01122832 0.01163864 0.01177073 0.01187468 0.01187825 0.01191521 0.01191378 0.01196361 0.01173592] mean value: 0.011542916297912598 key: test_mcc value: [0.66243303 0.51017582 0.61706091 0.37275718 0.66121206 0.34007914 0.40493069 0.49183384 0.52704628 0.54772256] mean value: 0.5135251490613606 key: train_mcc value: [0.75162538 0.53346267 0.69745701 0.71589767 0.70873277 0.43737334 0.47638707 0.66160331 0.69332659 0.74115186] mean value: 0.6417017668619277 key: test_fscore value: [0.83333333 0.77192982 0.8 0.70588235 0.84 0.51428571 0.52941176 0.73913043 0.77966102 0.79245283] mean value: 0.730608727174795 key: train_fscore value: [0.88069414 0.78358209 0.85321101 0.86462882 0.85909091 0.51567944 0.56565657 0.8042328 0.85080645 0.87555556] mean value: 0.7853137791511726 key: test_precision value: [0.8 0.64705882 0.81818182 0.64285714 0.80769231 0.81818182 0.9 0.77272727 0.63888889 0.7 ] mean value: 0.7545588072058661 key: train_precision value: [0.812 0.64615385 0.82666667 0.80161943 0.82173913 0.96103896 0.96551724 0.9047619 0.74035088 0.82426778] mean value: 0.8304115843253614 key: test_recall value: [0.86956522 0.95652174 0.7826087 0.7826087 0.875 0.375 0.375 0.70833333 1. 0.91304348] mean value: 0.7637681159420289 key: train_recall value: [0.96208531 0.99526066 0.88151659 0.93838863 0.9 0.35238095 0.4 0.72380952 1. 0.93364929] mean value: 0.8087090950124125 key: test_accuracy value: [0.82978723 0.72340426 0.80851064 0.68085106 0.82978723 0.63829787 0.65957447 0.74468085 0.7173913 0.76086957] mean value: 0.7393154486586495 key: train_accuracy value: [0.86935867 0.72446556 0.847981 0.85273159 0.85273159 0.66983373 0.6935867 0.82422803 0.82464455 0.86729858] mean value: 0.8026859992570161 key: test_roc_auc value: [0.83061594 0.72826087 0.80797101 0.68297101 0.82880435 0.64402174 0.66576087 0.74547101 0.7173913 0.76086957] mean value: 0.7412137681159421 key: train_roc_auc value: [0.86913789 0.72382081 0.84790115 0.85252765 0.8528436 0.66908147 0.692891 0.82399007 0.82464455 0.86729858] mean value: 0.8024136763710225 key: test_jcc value: [0.71428571 0.62857143 0.66666667 0.54545455 0.72413793 0.34615385 0.36 0.5862069 0.63888889 0.65625 ] mean value: 0.5866615917607296 key: train_jcc value: [0.78682171 0.64417178 0.744 0.76153846 0.75298805 0.34741784 0.3943662 0.67256637 0.74035088 0.77865613] mean value: 0.6622877406829983 MCC on Blind test: -0.29 MCC on Training: 0.51 Running classifier: 24 Model_name: XGBoost Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.10874605 0.10613203 0.10450745 0.09765816 0.24402666 0.08879781 0.09962225 0.09869647 0.12823462 0.09675479] mean value: 0.11731762886047363 key: score_time value: [0.01253653 0.01131558 0.01122522 0.0119369 0.01101589 0.0109818 0.01104259 0.01098967 0.01131225 0.01258612] mean value: 0.011494255065917969 key: test_mcc value: [0.87979456 0.95833333 0.91833182 0.7876601 0.95825929 0.91804649 0.8047833 0.91804649 0.91651514 0.95742711] mean value: 0.9017197636797546 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.93877551 0.9787234 0.95833333 0.88888889 0.97959184 0.96 0.90566038 0.96 0.95833333 0.9787234 ] mean value: 0.9507030088363461 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:463: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_CV['source_data'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:490: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_BT['source_data'] = 'BT' [0.88461538 0.95833333 0.92 0.90909091 0.96 0.92307692 0.82758621 0.92307692 0.92 0.95833333] mean value: 0.9184113013423361 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 0.86956522 1. 1. 1. 1. 1. 1. ] mean value: 0.9869565217391305 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.93617021 0.9787234 0.95744681 0.89361702 0.9787234 0.95744681 0.89361702 0.95744681 0.95652174 0.97826087] mean value: 0.9487974098057353 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.9375 0.97916667 0.95833333 0.89311594 0.97826087 0.95652174 0.89130435 0.95652174 0.95652174 0.97826087] mean value: 0.9485507246376812 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.88461538 0.95833333 0.92 0.8 0.96 0.92307692 0.82758621 0.92307692 0.92 0.95833333] mean value: 0.9075022104332451 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.12 MCC on Training: 0.9 Extracting tts_split_name: sl Total cols in each df: CV df: 8 metaDF: 17 Adding column: Model_name Total cols in bts df: BT_df: 8 First proceeding to rowbind CV and BT dfs: Final output should have: 25 columns Combinig 2 using pd.concat by row ~ rowbind Checking Dims of df to combine: Dim of CV: (24, 8) Dim of BT: (24, 8) 8 Number of Common columns: 8 These are: ['source_data', 'Precision', 'JCC', 'MCC', 'Recall', 'Accuracy', 'F1', 'ROC_AUC'] Concatenating dfs with different resampling methods [WF]: Split type: sl No. of dfs combining: 2 PASS: 2 dfs successfully combined nrows in combined_df_wf: 48 ncols in combined_df_wf: 8 PASS: proceeding to merge metadata with CV and BT dfs Adding column: Model_name ========================================================= SUCCESS: Ran multiple classifiers ======================================================= ============================================================== Running several classification models (n): 24 List of models: ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('Gaussian NB', GaussianNB()) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('K-Nearest Neighbors', KNeighborsClassifier()) ('LDA', LinearDiscriminantAnalysis()) ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(cv=3, random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Multinomial', MultinomialNB()) ('Naive Bayes', BernoulliNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=12, random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Random Forest', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42)) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=3)) ('SVC', SVC(random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=12, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ================================================================ Running classifier: 1 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.08912778 0.08611846 0.08659625 0.08703518 0.08907175 0.08845162 0.09100151 0.09375358 0.08978128 0.08897853] mean value: 0.08899159431457519 key: score_time value: [0.01449513 0.01455879 0.01447105 0.01480675 0.01568127 0.01509881 0.01498795 0.01467443 0.01589036 0.01604247] mean value: 0.015070700645446777 key: test_mcc value: [ 0.35355339 0.4472136 0.16903085 0. 0.63333333 0.69006556 0.44854261 0.26666667 0.83333333 -0.2608746 ] mean value: 0.3580864745912178 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.6 0.75 0.61538462 0.4 0.8 0.83333333 0.66666667 0.66666667 0.90909091 0.22222222] mean value: 0.6463364413364413 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.75 0.6 0.57142857 0.5 0.8 0.71428571 0.75 0.66666667 1. 0.33333333] mean value: 0.6685714285714286 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 1. 0.66666667 0.33333333 0.8 1. 0.6 0.66666667 0.83333333 0.16666667] mean value: 0.6566666666666666 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.66666667 0.66666667 0.58333333 0.5 0.81818182 0.81818182 0.72727273 0.63636364 0.90909091 0.36363636] mean value: 0.668939393939394 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.66666667 0.66666667 0.58333333 0.5 0.81666667 0.83333333 0.71666667 0.63333333 0.91666667 0.38333333] mean value: 0.6716666666666667 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.42857143 0.6 0.44444444 0.25 0.66666667 0.71428571 0.5 0.5 0.83333333 0.125 ] mean value: 0.5062301587301586 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.15 MCC on Training: 0.36 Running classifier: 2 Model_name: Bagging Classifier Model func: BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3) Running model pipeline: [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... 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Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... à˯Rª±`\g«U€Ì¯Rª[Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... ðY=ºU‘ÐM=ºU`l@0[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Ag®>žUa@\ãÀžU0Wœº[Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.4s remaining: 0.7s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.4s remaining: 0.7s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.7s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.4s remaining: 0.8s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.4s remaining: 0.7s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.4s remaining: 0.8s Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.4s remaining: 0.7s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.4s remaining: 0.8s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.4s remaining: 0.8s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.4s remaining: 0.8s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.5s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3))]) key: fit_time value: [0.10701585 0.14380741 0.15102553 0.10472345 0.15531969 0.16671729 0.14195514 0.16049576 0.15330982 0.14174914] mean value: 0.14261190891265868 key: score_time value: [0.05834317 0.05319881 0.04548478 0.0517261 0.04650068 0.06326032 0.07102752 0.04217267 0.03514409 0.08104992] mean value: 0.054790806770324704 key: test_mcc value: [0.35355339 0.4472136 0.33333333 0.35355339 0.26666667 0.26666667 0.44854261 0.1 0.46666667 0.63333333] mean value: 0.36695296569257024 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.6 0.75 0.66666667 0.6 0.6 0.6 0.66666667 0.54545455 0.72727273 0.83333333] mean value: 0.6589393939393939 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.75 0.6 0.66666667 0.75 0.6 0.6 0.75 0.6 0.8 0.83333333] mean value: 0.695 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 1. 0.66666667 0.5 0.6 0.6 0.6 0.5 0.66666667 0.83333333] mean value: 0.6466666666666667 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.66666667 0.66666667 0.66666667 0.66666667 0.63636364 0.63636364 0.72727273 0.54545455 0.72727273 0.81818182] mean value: 0.6757575757575758 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.66666667 0.66666667 0.66666667 0.66666667 0.63333333 0.63333333 0.71666667 0.55 0.73333333 0.81666667] mean value: 0.675 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.42857143 0.6 0.5 0.42857143 0.42857143 0.42857143 0.5 0.375 0.57142857 0.71428571] mean value: 0.49749999999999994 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.1 MCC on Training: 0.37 Running classifier: 3 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.0127213 0.01172757 0.01202726 0.0116272 0.01124167 0.01217961 0.01258373 0.01192975 0.01302385 0.01136041] mean value: 0.012042236328125 key: score_time value: [0.0087564 0.0083251 0.00940013 0.00861597 0.00863838 0.00856066 0.00826287 0.0084126 0.00888562 0.00862122] mean value: 0.008647894859313965 key: test_mcc value: [ 0.33333333 0.16903085 0.50709255 0. 0.3105295 -0.1 0.51639778 0.46666667 0.06900656 0.46666667] mean value: 0.2738723907582097 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.66666667 0.61538462 0.72727273 0.5 0.66666667 0.4 0.57142857 0.72727273 0.61538462 0.72727273] mean value: 0.6217349317349317 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.66666667 0.57142857 0.8 0.5 0.57142857 0.4 1. 0.8 0.57142857 0.8 ] mean value: 0.6680952380952381 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.66666667 0.66666667 0.66666667 0.5 0.8 0.4 0.4 0.66666667 0.66666667 0.66666667] mean value: 0.6100000000000001 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.66666667 0.58333333 0.75 0.5 0.63636364 0.45454545 0.72727273 0.72727273 0.54545455 0.72727273] mean value: 0.6318181818181817 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.66666667 0.58333333 0.75 0.5 0.65 0.45 0.7 0.73333333 0.53333333 0.73333333] mean value: 0.63 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.5 0.44444444 0.57142857 0.33333333 0.5 0.25 0.4 0.57142857 0.44444444 0.57142857] mean value: 0.45865079365079364 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.15 MCC on Training: 0.27 Running classifier: 4 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.00909805 0.00847673 0.00847745 0.00835299 0.00825596 0.00840735 0.00826073 0.00828338 0.00832939 0.00832653] mean value: 0.008426856994628907 key: score_time value: [0.00879335 0.00847459 0.00832391 0.00820231 0.00829434 0.00819397 0.00837016 0.00825167 0.00839972 0.00837469] mean value: 0.00836787223815918 key: test_mcc value: [ 0.16903085 -0.19245009 -0.33333333 0. -0.26666667 0.3105295 0.46666667 0.28867513 0.1 0.26666667] mean value: 0.08091187308480337 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.61538462 0.53333333 0.33333333 0.5 0.36363636 0.66666667 0.72727273 0.28571429 0.54545455 0.66666667] mean value: 0.5237462537462538 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.57142857 0.44444444 0.33333333 0.5 0.33333333 0.57142857 0.66666667 1. 0.6 0.66666667] mean value: 0.5687301587301586 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.66666667 0.66666667 0.33333333 0.5 0.4 0.8 0.8 0.16666667 0.5 0.66666667] mean value: 0.55 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.58333333 0.41666667 0.33333333 0.5 0.36363636 0.63636364 0.72727273 0.54545455 0.54545455 0.63636364] mean value: 0.5287878787878787 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.58333333 0.41666667 0.33333333 0.5 0.36666667 0.65 0.73333333 0.58333333 0.55 0.63333333] mean value: 0.5349999999999999 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.44444444 0.36363636 0.2 0.33333333 0.22222222 0.5 0.57142857 0.16666667 0.375 0.5 ] mean value: 0.36767316017316015 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.06 MCC on Training: 0.08 Running classifier: 5 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.08945203 0.08815145 0.0892942 0.08878064 0.08595371 0.09253383 0.09236741 0.0940907 0.09644175 0.09366679] mean value: 0.09107325077056885 key: score_time value: [0.01685214 0.01857734 0.01735735 0.0226264 0.0171833 0.01873589 0.01904035 0.01902604 0.01924753 0.0184803 ] mean value: 0.018712663650512697 key: test_mcc value: [ 0.50709255 -0.35355339 0.35355339 0.16903085 0.06900656 0.44854261 -0.1490712 0.3105295 0.55901699 0.26666667] mean value: 0.21808145375352664 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.72727273 0.42857143 0.6 0.61538462 0.44444444 0.66666667 0.25 0.6 0.66666667 0.66666667] mean value: 0.5665673215673216 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.8 0.375 0.75 0.57142857 0.5 0.75 0.33333333 0.75 1. 0.66666667] mean value: 0.6496428571428571 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.66666667 0.5 0.5 0.66666667 0.4 0.6 0.2 0.5 0.5 0.66666667] mean value: 0.52 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.75 0.33333333 0.66666667 0.58333333 0.54545455 0.72727273 0.45454545 0.63636364 0.72727273 0.63636364] mean value: 0.6060606060606061 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.75 0.33333333 0.66666667 0.58333333 0.53333333 0.71666667 0.43333333 0.65 0.75 0.63333333] mean value: 0.6049999999999999 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.57142857 0.27272727 0.42857143 0.44444444 0.28571429 0.5 0.14285714 0.42857143 0.5 0.5 ] mean value: 0.40743145743145737 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.19 MCC on Training: 0.22 Running classifier: 6 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.21717167 0.21926379 0.22239876 0.21982145 0.22162819 0.22074294 0.22452569 0.21901202 0.21670341 0.22056532] mean value: 0.22018332481384278 key: score_time value: [0.00896549 0.00968862 0.00916028 0.00923944 0.0089643 0.00919127 0.00935078 0.00893426 0.00905967 0.0090766 ] mean value: 0.00916306972503662 key: test_mcc value: [0.35355339 0.70710678 0.33333333 0.33333333 0.1 0.3105295 0.44854261 0.3105295 0.63333333 0.1 ] mean value: 0.36302617887604705 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.6 0.85714286 0.66666667 0.66666667 0.54545455 0.66666667 0.66666667 0.6 0.83333333 0.54545455] mean value: 0.6648051948051947 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.75 0.75 0.66666667 0.66666667 0.5 0.57142857 0.75 0.75 0.83333333 0.6 ] mean value: 0.6838095238095236 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 1. 0.66666667 0.66666667 0.6 0.8 0.6 0.5 0.83333333 0.5 ] mean value: 0.6666666666666666 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.66666667 0.83333333 0.66666667 0.66666667 0.54545455 0.63636364 0.72727273 0.63636364 0.81818182 0.54545455] mean value: 0.6742424242424242 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.66666667 0.83333333 0.66666667 0.66666667 0.55 0.65 0.71666667 0.65 0.81666667 0.55 ] mean value: 0.6766666666666666 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.42857143 0.75 0.5 0.5 0.375 0.5 0.5 0.42857143 0.71428571 0.375 ] mean value: 0.5071428571428572 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.19 MCC on Training: 0.36 Running classifier: 7 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.00923705 0.00806069 0.0082674 0.00823569 0.00817275 0.0080893 0.00820613 0.00831771 0.00820684 0.00834441] mean value: 0.008313798904418945 key: score_time value: [0.00882483 0.00826645 0.00827432 0.00829077 0.00829339 0.00819278 0.00900388 0.00822854 0.0083313 0.00823307] mean value: 0.00839393138885498 key: test_mcc value: [ 0. -0.16903085 -0.19245009 0.50709255 -0.1 0.67082039 -0.1490712 0.1490712 0.1 -0.26666667] mean value: 0.05497653387448016 key: train_mcc value: [0.43549417 0.3737002 0.4819316 0.42257713 0.50193406 0.48082109 0.54118935 0.45723265 0.3822992 0.46468836] mean value: 0.4541867803250604 key: test_fscore value: [0.25 0.36363636 0.22222222 0.72727273 0.4 0.75 0.25 0.44444444 0.54545455 0.36363636] mean value: 0.43166666666666664 key: train_fscore value: [0.69473684 0.67346939 0.7032967 0.62650602 0.72916667 0.72164948 0.75 0.71428571 0.65957447 0.58666667] mean value: 0.6859351957493691 key: test_precision value: [0.5 0.4 0.33333333 0.8 0.4 1. 0.33333333 0.66666667 0.6 0.4 ] mean value: 0.5433333333333333 key: train_precision value: [0.75 0.70212766 0.8 0.8125 0.79545455 0.77777778 0.81818182 0.74468085 0.72093023 0.91666667] mean value: 0.7838319551277245 key: test_recall value: [0.16666667 0.33333333 0.16666667 0.66666667 0.4 0.6 0.2 0.33333333 0.5 0.33333333] mean value: 0.37 key: train_recall value: [0.64705882 0.64705882 0.62745098 0.50980392 0.67307692 0.67307692 0.69230769 0.68627451 0.60784314 0.43137255] mean value: 0.6195324283559578 key: test_accuracy value: [0.5 0.41666667 0.41666667 0.75 0.45454545 0.81818182 0.45454545 0.54545455 0.54545455 0.36363636] mean value: 0.5265151515151514 key: train_accuracy value: [0.71568627 0.68627451 0.73529412 0.69607843 0.74757282 0.73786408 0.76699029 0.72815534 0.68932039 0.69902913] mean value: 0.7202265372168284 key: test_roc_auc value: [0.5 0.41666667 0.41666667 0.75 0.45 0.8 0.43333333 0.56666667 0.55 0.36666667] mean value: 0.525 key: train_roc_auc value: [0.71568627 0.68627451 0.73529412 0.69607843 0.74830317 0.73849925 0.76772247 0.72775264 0.68853695 0.69645551] mean value: 0.7200603318250377 key: test_jcc value: [0.14285714 0.22222222 0.125 0.57142857 0.25 0.6 0.14285714 0.28571429 0.375 0.22222222] mean value: 0.29373015873015873 key: train_jcc value: [0.53225806 0.50769231 0.54237288 0.45614035 0.57377049 0.56451613 0.6 0.55555556 0.49206349 0.41509434] mean value: 0.5239463612518788 MCC on Blind test: 0.47 MCC on Training: 0.05 Running classifier: 8 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.04843998 0.03902078 0.03892517 0.0396409 0.03953886 0.03969908 0.04017234 0.04046893 0.03937984 0.03937387] mean value: 0.04046597480773926 key: score_time value: [0.01892209 0.02335835 0.02187514 0.02334356 0.01972318 0.01176596 0.02319169 0.02210259 0.02184391 0.02178764] mean value: 0.02079141139984131 key: test_mcc value: [ 0. -0.16903085 0.50709255 0.16903085 0.3105295 0.63333333 -0.1 0.1490712 0.46666667 0.1 ] mean value: 0.20666932530411555 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.4 0.46153846 0.72727273 0.61538462 0.66666667 0.8 0.4 0.44444444 0.72727273 0.54545455] mean value: 0.5788034188034189 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.5 0.42857143 0.8 0.57142857 0.57142857 0.8 0.4 0.66666667 0.8 0.6 ] mean value: 0.6138095238095238 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.33333333 0.5 0.66666667 0.66666667 0.8 0.8 0.4 0.33333333 0.66666667 0.5 ] mean value: 0.5666666666666667 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.5 0.41666667 0.75 0.58333333 0.63636364 0.81818182 0.45454545 0.54545455 0.72727273 0.54545455] mean value: 0.5977272727272729 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.5 0.41666667 0.75 0.58333333 0.65 0.81666667 0.45 0.56666667 0.73333333 0.55 ] mean value: 0.6016666666666667 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.25 0.3 0.57142857 0.44444444 0.5 0.66666667 0.25 0.28571429 0.57142857 0.375 ] mean value: 0.42146825396825394 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.07 MCC on Training: 0.21 Running classifier: 9 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.01607442 0.00892425 0.00817323 0.00819945 0.0081563 0.00882173 0.00898719 0.00861692 0.00832844 0.00906086] mean value: 0.009334278106689454 key: score_time value: [0.02082205 0.0146203 0.01459074 0.01473641 0.00927806 0.00979924 0.00991488 0.00910735 0.00948572 0.01030445] mean value: 0.012265920639038086 key: test_mcc value: [ 0.19245009 -0.50709255 0. 0. 0.06900656 0.2608746 -0.1490712 0.1 0.46666667 -0.1 ] mean value: 0.033283415836865685 key: train_mcc value: [0.43549417 0.4314555 0.4819316 0.47809144 0.42093969 0.46267878 0.4234235 0.49528156 0.39932387 0.47744421] mean value: 0.4506064303169358 key: test_fscore value: [0.44444444 0.30769231 0.5 0.5 0.44444444 0.5 0.25 0.54545455 0.72727273 0.5 ] mean value: 0.4719308469308469 key: train_fscore value: [0.69473684 0.71287129 0.7032967 0.70967742 0.69387755 0.70833333 0.6875 0.74 0.68041237 0.72164948] mean value: 0.7052354991909363 key: test_precision value: [0.66666667 0.28571429 0.5 0.5 0.5 0.66666667 0.33333333 0.6 0.8 0.5 ] mean value: 0.5352380952380952 key: train_precision value: [0.75 0.72 0.8 0.78571429 0.73913043 0.77272727 0.75 0.75510204 0.7173913 0.76086957] mean value: 0.7550934903605712 key: test_recall value: [0.33333333 0.33333333 0.5 0.5 0.4 0.4 0.2 0.5 0.66666667 0.5 ] mean value: 0.4333333333333333 key: train_recall value: [0.64705882 0.70588235 0.62745098 0.64705882 0.65384615 0.65384615 0.63461538 0.7254902 0.64705882 0.68627451] mean value: 0.6628582202111615 key: test_accuracy value: [0.58333333 0.25 0.5 0.5 0.54545455 0.63636364 0.45454545 0.54545455 0.72727273 0.45454545] mean value: 0.5196969696969698 key: train_accuracy value: [0.71568627 0.71568627 0.73529412 0.73529412 0.70873786 0.72815534 0.70873786 0.74757282 0.69902913 0.73786408] mean value: 0.7232057871692366 key: test_roc_auc value: [0.58333333 0.25 0.5 0.5 0.53333333 0.61666667 0.43333333 0.55 0.73333333 0.45 ] mean value: 0.515 key: train_roc_auc value: [0.71568627 0.71568627 0.73529412 0.73529412 0.70927602 0.72888386 0.70946456 0.74736048 0.69852941 0.73736802] mean value: 0.7232843137254902 key: test_jcc value: [0.28571429 0.18181818 0.33333333 0.33333333 0.28571429 0.33333333 0.14285714 0.375 0.57142857 0.33333333] mean value: 0.3175865800865801 key: train_jcc value: [0.53225806 0.55384615 0.54237288 0.55 0.53125 0.5483871 0.52380952 0.58730159 0.515625 0.56451613] mean value: 0.5449366436635777 MCC on Blind test: 0.19 MCC on Training: 0.03 Running classifier: 10 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.02401614 0.06474233 0.04212976 0.02330947 0.04098392 0.02151322 0.02075768 0.01983929 0.04702592 0.04422808] mean value: 0.034854578971862796 key: score_time value: [0.02087712 0.02011681 0.01194143 0.022192 0.01192188 0.01186609 0.01180553 0.01193285 0.02218008 0.02245426] mean value: 0.016728806495666503 key: test_mcc value: [-0.16903085 0. 0. 0. 0.06900656 -0.06900656 -0.1 0.46666667 0.43033148 0.1 ] mean value: 0.07279672986328986 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.46153846 0.5 0.5 0.4 0.44444444 0.5 0.4 0.72727273 0.5 0.54545455] mean value: 0.49787101787101784 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.42857143 0.5 0.5 0.5 0.5 0.42857143 0.4 0.8 1. 0.6 ] mean value: 0.5657142857142857 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 0.5 0.5 0.33333333 0.4 0.6 0.4 0.66666667 0.33333333 0.5 ] mean value: 0.4733333333333333 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.41666667 0.5 0.5 0.5 0.54545455 0.45454545 0.45454545 0.72727273 0.63636364 0.54545455] mean value: 0.5280303030303031 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.41666667 0.5 0.5 0.5 0.53333333 0.46666667 0.45 0.73333333 0.66666667 0.55 ] mean value: 0.5316666666666666 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.3 0.33333333 0.33333333 0.25 0.28571429 0.33333333 0.25 0.57142857 0.33333333 0.375 ] mean value: 0.33654761904761904 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.1 MCC on Training: 0.07 Running classifier: 11 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.05498624 0.03180146 0.02629781 0.02687049 0.03174233 0.05573058 0.02790523 0.02850294 0.02924418 0.02594161] mean value: 0.03390228748321533 key: score_time value: [0.02004528 0.01199102 0.01193428 0.0117228 0.01190257 0.01181221 0.01172638 0.01169801 0.01179123 0.01170754] mean value: 0.012633132934570312 key: test_mcc value: [ 0.19245009 0.35355339 0.16903085 0. -0.1 0.63333333 0.1 0.46666667 0.1490712 0.26666667] mean value: 0.2230772196435505 key: train_mcc value: [0.76706883 0.70929937 0.68627451 0.7856742 0.66993592 0.65107511 0.63122172 0.65081204 0.65045249 0.78760596] mean value: 0.6989420144080876 key: test_fscore value: [0.44444444 0.71428571 0.61538462 0.5 0.4 0.8 0.54545455 0.72727273 0.44444444 0.66666667] mean value: 0.5857953157953159 key: train_fscore value: [0.87755102 0.84536082 0.84313725 0.8952381 0.83809524 0.82352941 0.81553398 0.82 0.82352941 0.88888889] mean value: 0.847086412638655 key: test_precision value: [0.66666667 0.625 0.57142857 0.5 0.4 0.8 0.5 0.8 0.66666667 0.66666667] mean value: 0.6196428571428572 key: train_precision value: [0.91489362 0.89130435 0.84313725 0.87037037 0.83018868 0.84 0.82352941 0.83673469 0.82352941 0.91666667] mean value: 0.8590354453438607 /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( key: test_recall value: [0.33333333 0.83333333 0.66666667 0.5 0.4 0.8 0.6 0.66666667 0.33333333 0.66666667] mean value: 0.58 key: train_recall value: [0.84313725 0.80392157 0.84313725 0.92156863 0.84615385 0.80769231 0.80769231 0.80392157 0.82352941 0.8627451 ] mean value: 0.8363499245852186 key: test_accuracy value: [0.58333333 0.66666667 0.58333333 0.5 0.45454545 0.81818182 0.54545455 0.72727273 0.54545455 0.63636364] mean value: 0.6060606060606061 key: train_accuracy value: [0.88235294 0.85294118 0.84313725 0.89215686 0.83495146 0.82524272 0.81553398 0.82524272 0.82524272 0.89320388] mean value: 0.8490005711022274 key: test_roc_auc value: [0.58333333 0.66666667 0.58333333 0.5 0.45 0.81666667 0.55 0.73333333 0.56666667 0.63333333] mean value: 0.6083333333333332 key: train_roc_auc value: [0.88235294 0.85294118 0.84313725 0.89215686 0.83484163 0.82541478 0.81561086 0.82503771 0.82522624 0.89291101] mean value: 0.8489630467571644 key: test_jcc value: [0.28571429 0.55555556 0.44444444 0.33333333 0.25 0.66666667 0.375 0.57142857 0.28571429 0.5 ] mean value: 0.4267857142857142 key: train_jcc value: [0.78181818 0.73214286 0.72881356 0.81034483 0.72131148 0.7 0.68852459 0.69491525 0.7 0.8 ] mean value: 0.7357870745680339 MCC on Blind test: 0.23 MCC on Training: 0.22 Running classifier: 12 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(cv=3, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(cv=3, random_state=42))]) key: fit_time value: [0.57813764 0.38531542 0.41018772 0.40786695 0.38914442 0.56891227 0.38605857 0.42196631 0.45527506 0.40960479] mean value: 0.4412469148635864 key: score_time value: [0.0127697 0.01203299 0.01213884 0.01198792 0.0117743 0.01222777 0.01236725 0.0120194 0.0118835 0.01195335] mean value: 0.01211550235748291 key: test_mcc value: [ 0.19245009 0.35355339 0. -0.16903085 -0.26666667 0.44854261 0.44854261 0.3105295 0.55901699 0.26666667] mean value: 0.2143604352601513 key: train_mcc value: [0.70710678 0.74870489 0.62853936 0.96152395 0.65045249 0.72878164 0.57336858 0.78649572 0.63189883 0.82534898] mean value: 0.7242221218695242 key: test_fscore value: [0.44444444 0.71428571 0.5 0.36363636 0.36363636 0.66666667 0.66666667 0.6 0.66666667 0.66666667] mean value: 0.5652669552669554 key: train_fscore value: [0.84848485 0.86597938 0.80808081 0.98 0.82692308 0.8627451 0.78431373 0.89108911 0.80808081 0.91089109] mean value: 0.8586587944562055 key: test_precision value: [0.66666667 0.625 0.5 0.4 0.33333333 0.75 0.75 0.75 1. 0.66666667] mean value: 0.6441666666666667 key: train_precision value: [0.875 0.91304348 0.83333333 1. 0.82692308 0.88 0.8 0.9 0.83333333 0.92 ] mean value: 0.8781633221850613 key: test_recall value: [0.33333333 0.83333333 0.5 0.33333333 0.4 0.6 0.6 0.5 0.5 0.66666667] mean value: 0.5266666666666666 key: train_recall value: [0.82352941 0.82352941 0.78431373 0.96078431 0.82692308 0.84615385 0.76923077 0.88235294 0.78431373 0.90196078] mean value: 0.8403092006033182 key: test_accuracy value: [0.58333333 0.66666667 0.5 0.41666667 0.36363636 0.72727273 0.72727273 0.63636364 0.72727273 0.63636364] mean value: 0.5984848484848485 key: train_accuracy value: [0.85294118 0.87254902 0.81372549 0.98039216 0.82524272 0.86407767 0.78640777 0.89320388 0.81553398 0.91262136] mean value: 0.861669522177803 key: test_roc_auc value: [0.58333333 0.66666667 0.5 0.41666667 0.36666667 0.71666667 0.71666667 0.65 0.75 0.63333333] mean value: 0.6 key: train_roc_auc value: [0.85294118 0.87254902 0.81372549 0.98039216 0.82522624 0.86425339 0.78657617 0.89309955 0.81523379 0.91251885] mean value: 0.8616515837104071 key: test_jcc value: [0.28571429 0.55555556 0.33333333 0.22222222 0.22222222 0.5 0.5 0.42857143 0.5 0.5 ] mean value: 0.40476190476190477 key: train_jcc value: [0.73684211 0.76363636 0.6779661 0.96078431 0.70491803 0.75862069 0.64516129 0.80357143 0.6779661 0.83636364] mean value: 0.7565830063714545 MCC on Blind test: 0.06 MCC on Training: 0.21 Running classifier: 13 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [0.58288598 1.01259995 0.57898593 0.63764811 0.59210062 0.72052288 0.604532 0.61391139 0.79486847 0.59596562] mean value: 0.6734020948410034 key: score_time value: [0.01254606 0.01242018 0.01212859 0.01201797 0.01206827 0.01215839 0.01290512 0.01300263 0.01309371 0.01196551] mean value: 0.012430644035339356 key: test_mcc value: [ 0.35355339 0. 0. 0.16903085 -0.26666667 0.44854261 -0.1 0.3105295 0.55901699 0.26666667] mean value: 0.17406733511905143 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.6 0.57142857 0.5 0.54545455 0.36363636 0.66666667 0.4 0.6 0.66666667 0.66666667] mean value: 0.5580519480519481 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.75 0.5 0.5 0.6 0.33333333 0.75 0.4 0.75 1. 0.66666667] mean value: 0.6250000000000001 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 0.66666667 0.5 0.5 0.4 0.6 0.4 0.5 0.5 0.66666667] mean value: 0.5233333333333333 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.66666667 0.5 0.5 0.58333333 0.36363636 0.72727273 0.45454545 0.63636364 0.72727273 0.63636364] mean value: 0.5795454545454546 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.66666667 0.5 0.5 0.58333333 0.36666667 0.71666667 0.45 0.65 0.75 0.63333333] mean value: 0.5816666666666667 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( [0.42857143 0.4 0.33333333 0.375 0.22222222 0.5 0.25 0.42857143 0.5 0.5 ] mean value: 0.39376984126984127 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.02 MCC on Training: 0.17 Running classifier: 14 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.0116899 0.01146245 0.00856376 0.00857997 0.00804377 0.00812292 0.00809026 0.0081377 0.00809097 0.00808001] mean value: 0.008886170387268067 key: score_time value: [0.01148057 0.01129651 0.00866938 0.00848651 0.00807238 0.00816202 0.00808907 0.00810957 0.00827265 0.00812531] mean value: 0.008876395225524903 key: test_mcc value: [ 0.19245009 -0.16903085 -0.16903085 0.16903085 -0.26666667 0.3105295 0.51639778 0.55901699 -0.26666667 -0.1 ] mean value: 0.07760301810241682 key: train_mcc value: [0.45237392 0.29780434 0.37515429 0.41464421 0.4234235 0.49697785 0.46267878 0.22387638 0.22387638 0.30542083] mean value: 0.36762304898361553 key: test_fscore value: [0.44444444 0.36363636 0.36363636 0.61538462 0.36363636 0.66666667 0.57142857 0.66666667 0.36363636 0.5 ] mean value: 0.4919136419136419 key: train_fscore value: [0.71428571 0.61702128 0.66666667 0.6875 0.6875 0.74 0.70833333 0.58333333 0.58333333 0.60869565] mean value: 0.6596669309722037 key: test_precision value: [0.66666667 0.4 0.4 0.57142857 0.33333333 0.57142857 1. 1. 0.4 0.5 ] mean value: 0.5842857142857143 key: train_precision value: [0.74468085 0.6744186 0.71111111 0.73333333 0.75 0.77083333 0.77272727 0.62222222 0.62222222 0.68292683] mean value: 0.708447577993278 key: test_recall value: [0.33333333 0.33333333 0.33333333 0.66666667 0.4 0.8 0.4 0.5 0.33333333 0.5 ] mean value: 0.45999999999999996 key: train_recall value: [0.68627451 0.56862745 0.62745098 0.64705882 0.63461538 0.71153846 0.65384615 0.54901961 0.54901961 0.54901961] mean value: 0.6176470588235293 key: test_accuracy value: [0.58333333 0.41666667 0.41666667 0.58333333 0.36363636 0.63636364 0.72727273 0.72727273 0.36363636 0.45454545] mean value: 0.5272727272727272 key: train_accuracy value: [0.7254902 0.64705882 0.68627451 0.70588235 0.70873786 0.74757282 0.72815534 0.61165049 0.61165049 0.65048544] mean value: 0.6822958309537407 key: test_roc_auc value: [0.58333333 0.41666667 0.41666667 0.58333333 0.36666667 0.65 0.7 0.75 0.36666667 0.45 ] mean value: 0.5283333333333333 key: train_roc_auc value: [0.7254902 0.64705882 0.68627451 0.70588235 0.70946456 0.74792609 0.72888386 0.61104827 0.61104827 0.6495098 ] mean value: 0.6822586726998492 key: test_jcc value: [0.28571429 0.22222222 0.22222222 0.44444444 0.22222222 0.5 0.4 0.5 0.22222222 0.33333333] mean value: 0.3352380952380952 key: train_jcc value: [0.55555556 0.44615385 0.5 0.52380952 0.52380952 0.58730159 0.5483871 0.41176471 0.41176471 0.4375 ] mean value: 0.49460465451689367 MCC on Blind test: 0.07 MCC on Training: 0.08 Running classifier: 15 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.00849485 0.00821733 0.00819421 0.00867581 0.00827456 0.00854588 0.00818372 0.0086987 0.00836682 0.00846028] mean value: 0.008411216735839843 key: score_time value: [0.00837922 0.00835872 0.00824094 0.00832653 0.00838637 0.0082202 0.00829983 0.00822568 0.00832725 0.00834107] mean value: 0.008310580253601074 key: test_mcc value: [-0.30151134 0. 0.19245009 0.30151134 0.34641016 0.44854261 0.04303315 0.28867513 0.28867513 0.1 ] mean value: 0.17077862822970005 key: train_mcc value: [0.43937478 0.42905817 0.36369648 0.43305019 0.41366795 0.39112725 0.43052393 0.37282099 0.45941898 0.4465795 ] mean value: 0.4179318219923102 key: test_fscore value: [0. 0.4 0.44444444 0.28571429 0.33333333 0.66666667 0.28571429 0.28571429 0.28571429 0.54545455] mean value: 0.35327561327561324 key: train_fscore value: [0.60759494 0.54794521 0.5 0.57894737 0.58227848 0.53333333 0.6 0.53333333 0.625 0.53521127] mean value: 0.5643643925894324 key: test_precision value: [0. 0.5 0.66666667 1. 1. 0.75 0.5 1. 1. 0.6 ] mean value: 0.7016666666666665 key: train_precision value: [0.85714286 0.90909091 0.85714286 0.88 0.85185185 0.86956522 0.85714286 0.83333333 0.86206897 0.95 ] mean value: 0.8727338848613211 key: test_recall value: [0. 0.33333333 0.33333333 0.16666667 0.2 0.6 0.2 0.16666667 0.16666667 0.5 ] mean value: 0.26666666666666666 key: train_recall value: [0.47058824 0.39215686 0.35294118 0.43137255 0.44230769 0.38461538 0.46153846 0.39215686 0.49019608 0.37254902] mean value: 0.4190422322775264 key: test_accuracy value: [0.41666667 0.5 0.58333333 0.58333333 0.63636364 0.72727273 0.54545455 0.54545455 0.54545455 0.54545455] mean value: 0.5628787878787879 key: train_accuracy value: [0.69607843 0.67647059 0.64705882 0.68627451 0.67961165 0.66019417 0.68932039 0.66019417 0.70873786 0.67961165] mean value: 0.6783552255853798 key: test_roc_auc value: [0.41666667 0.5 0.58333333 0.58333333 0.6 0.71666667 0.51666667 0.58333333 0.58333333 0.55 ] mean value: 0.5633333333333332 key: train_roc_auc value: [0.69607843 0.67647059 0.64705882 0.68627451 0.68193816 0.66289593 0.69155354 0.65761689 0.7066365 0.67665913] mean value: 0.6783182503770739 key: test_jcc value: [0. 0.25 0.28571429 0.16666667 0.2 0.5 0.16666667 0.16666667 0.16666667 0.375 ] mean value: 0.22773809523809524 key: train_jcc value: [0.43636364 0.37735849 0.33333333 0.40740741 0.41071429 0.36363636 0.42857143 0.36363636 0.45454545 0.36538462] mean value: 0.3940951379158927 MCC on Blind test: -0.01 MCC on Training: 0.17 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=12, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.00914168 0.01278067 0.01422644 0.01437736 0.01347733 0.01254034 0.01379728 0.01317263 0.01347995 0.01392317] mean value: 0.013091683387756348 key: score_time value: [0.00883627 0.01149249 0.01164246 0.01147223 0.01143503 0.01168919 0.0120275 0.01182413 0.01165414 0.01171041] mean value: 0.01137838363647461 key: test_mcc value: [0.30151134 0.35355339 0.16903085 0.19245009 0.06900656 0.34641016 0.2608746 0.06900656 0.43033148 0.26666667] mean value: 0.24588416961824397 key: train_mcc value: [0.43133109 0.66679486 0.73056362 0.5395732 0.65388774 0.49991905 0.65298053 0.60212124 0.5436369 0.64522178] mean value: 0.5966030003982344 key: test_fscore value: [0.28571429 0.71428571 0.61538462 0.44444444 0.44444444 0.33333333 0.5 0.61538462 0.5 0.66666667] mean value: 0.511965811965812 key: train_fscore value: [0.47761194 0.83168317 0.87037037 0.62162162 0.76744186 0.60526316 0.79120879 0.81081081 0.70588235 0.79120879] mean value: 0.7273102865136754 key: test_precision value: [1. 0.625 0.57142857 0.66666667 0.5 1. 0.66666667 0.57142857 1. 0.66666667] mean value: 0.7267857142857144 key: train_precision value: [1. 0.84 0.8245614 1. 0.97058824 0.95833333 0.92307692 0.75 0.88235294 0.9 ] mean value: 0.9048912836389617 key: test_recall value: [0.16666667 0.83333333 0.66666667 0.33333333 0.4 0.2 0.4 0.66666667 0.33333333 0.66666667] mean value: 0.4666666666666667 key: train_recall value: [0.31372549 0.82352941 0.92156863 0.45098039 0.63461538 0.44230769 0.69230769 0.88235294 0.58823529 0.70588235] mean value: 0.645550527903469 key: test_accuracy value: [0.58333333 0.66666667 0.58333333 0.58333333 0.54545455 0.63636364 0.63636364 0.54545455 0.63636364 0.63636364] mean value: 0.6053030303030303 key: train_accuracy value: [0.65686275 0.83333333 0.8627451 0.7254902 0.80582524 0.70873786 0.81553398 0.7961165 0.75728155 0.81553398] mean value: 0.7777460498762612 key: test_roc_auc value: [0.58333333 0.66666667 0.58333333 0.58333333 0.53333333 0.6 0.61666667 0.53333333 0.66666667 0.63333333] mean value: 0.6 key: train_roc_auc value: [0.65686275 0.83333333 0.8627451 0.7254902 0.80750377 0.71134992 0.81674208 0.7969457 0.75565611 0.81447964] mean value: 0.7781108597285067 key: test_jcc value: [0.16666667 0.55555556 0.44444444 0.28571429 0.28571429 0.2 0.33333333 0.44444444 0.33333333 0.5 ] mean value: 0.35492063492063497 key: train_jcc value: [0.31372549 0.71186441 0.7704918 0.45098039 0.62264151 0.43396226 0.65454545 0.68181818 0.54545455 0.65454545] mean value: 0.5840029502359834 MCC on Blind test: 0.01 MCC on Training: 0.25 Running classifier: 17 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.01228309 0.01597071 0.01526999 0.01624775 0.01502919 0.01525378 0.01511931 0.01510882 0.01580048 0.01518917] mean value: 0.015127229690551757 key: score_time value: [0.01178241 0.01197052 0.01191258 0.01194906 0.01181674 0.01208568 0.01190209 0.0120554 0.01212049 0.01186514] mean value: 0.011946010589599609 key: test_mcc value: [ 0.50709255 0.50709255 0. 0.66666667 0.44854261 -0.06900656 0.26666667 -0.44854261 0.06900656 0.26666667] mean value: 0.22141851056742196 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.76923077 0.76923077 0.5 0.83333333 0.66666667 0.5 0.6 0.2 0.61538462 0.66666667] mean value: 0.612051282051282 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.71428571 0.71428571 0.5 0.83333333 0.75 0.42857143 0.6 0.25 0.57142857 0.66666667] mean value: 0.6028571428571429 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.83333333 0.83333333 0.5 0.83333333 0.6 0.6 0.6 0.16666667 0.66666667 0.66666667] mean value: 0.6300000000000001 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.75 0.75 0.5 0.83333333 0.72727273 0.45454545 0.63636364 0.27272727 0.54545455 0.63636364] mean value: 0.6106060606060606 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.75 0.75 0.5 0.83333333 0.71666667 0.46666667 0.63333333 0.28333333 0.53333333 0.63333333] mean value: 0.6100000000000001 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.625 0.625 0.33333333 0.71428571 0.5 0.33333333 0.42857143 0.11111111 0.44444444 0.5 ] mean value: 0.4615079365079365 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.27 MCC on Training: 0.22 Running classifier: 18 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42))]) key: fit_time value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( [0.58708692 0.59696817 0.57624459 0.59182286 0.56788397 0.58691216 0.58645177 0.69270372 0.59268045 0.56219935] mean value: 0.5940953969955445 key: score_time value: [0.1849792 0.12759566 0.17200994 0.14777541 0.15130997 0.19572353 0.18150187 0.16427231 0.15789986 0.1360147 ] mean value: 0.16190824508666993 key: test_mcc value: [ 0.35355339 -0.19245009 0.35355339 -0.16903085 0.1 0.63333333 0.06900656 0.46666667 0.46666667 0.46666667] mean value: 0.2547965729778538 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.6 0.53333333 0.6 0.36363636 0.54545455 0.8 0.44444444 0.72727273 0.72727273 0.72727273] mean value: 0.606868686868687 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.75 0.44444444 0.75 0.4 0.5 0.8 0.5 0.8 0.8 0.8 ] mean value: 0.6544444444444444 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 0.66666667 0.5 0.33333333 0.6 0.8 0.4 0.66666667 0.66666667 0.66666667] mean value: 0.5800000000000001 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.66666667 0.41666667 0.66666667 0.41666667 0.54545455 0.81818182 0.54545455 0.72727273 0.72727273 0.72727273] mean value: 0.6257575757575757 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.66666667 0.41666667 0.66666667 0.41666667 0.55 0.81666667 0.53333333 0.73333333 0.73333333 0.73333333] mean value: 0.6266666666666667 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.42857143 0.36363636 0.42857143 0.22222222 0.375 0.66666667 0.28571429 0.57142857 0.57142857 0.57142857] mean value: 0.44846681096681096 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.1 MCC on Training: 0.25 Running classifier: 19 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea...age_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42))]) key: fit_time value: [0.92752051 0.94984365 0.95938301 0.90850997 0.98886585 0.90969968 0.91574359 0.87520289 0.93155503 0.95061946] mean value: 0.9316943645477295 key: score_time value: [0.15174055 0.22638202 0.23061943 0.17356467 0.21911645 0.17778516 0.19466639 0.21305919 0.13158679 0.21568298] mean value: 0.1934203624725342 key: test_mcc value: [ 0.35355339 0. 0.35355339 0.16903085 -0.06900656 0.26666667 0.2608746 0.63333333 0.46666667 0.63333333] mean value: 0.3068005673572991 key: train_mcc value: [0.88507941 0.88507941 0.92227807 0.92227807 0.88419471 0.84534144 0.88419471 0.94190878 0.88410778 0.92232278] mean value: 0.8976785160992385 key: test_fscore value: [0.6 0.625 0.6 0.54545455 0.5 0.6 0.5 0.83333333 0.72727273 0.83333333] mean value: 0.636439393939394 key: train_fscore value: [0.93877551 0.93877551 0.96 0.96 0.94117647 0.92156863 0.94117647 0.97029703 0.94 0.96078431] mean value: 0.9472553932464075 key: test_precision value: [0.75 0.5 0.75 0.6 0.42857143 0.6 0.66666667 0.83333333 0.8 0.83333333] mean value: 0.6761904761904761 key: train_precision value: [0.9787234 0.9787234 0.97959184 0.97959184 0.96 0.94 0.96 0.98 0.95918367 0.96078431] mean value: 0.9676598469174904 key: test_recall value: [0.5 0.83333333 0.5 0.5 0.6 0.6 0.4 0.83333333 0.66666667 0.83333333] mean value: 0.6266666666666667 key: train_recall value: [0.90196078 0.90196078 0.94117647 0.94117647 0.92307692 0.90384615 0.92307692 0.96078431 0.92156863 0.96078431] mean value: 0.9279411764705883 key: test_accuracy value: [0.66666667 0.5 0.66666667 0.58333333 0.45454545 0.63636364 0.63636364 0.81818182 0.72727273 0.81818182] mean value: 0.6507575757575758 key: train_accuracy value: [0.94117647 0.94117647 0.96078431 0.96078431 0.94174757 0.9223301 0.94174757 0.97087379 0.94174757 0.96116505] mean value: 0.9483533219112887 key: test_roc_auc value: [0.66666667 0.5 0.66666667 0.58333333 0.46666667 0.63333333 0.61666667 0.81666667 0.73333333 0.81666667] mean value: 0.65 key: train_roc_auc value: [0.94117647 0.94117647 0.96078431 0.96078431 0.94193062 0.92251131 0.94193062 0.97077677 0.94155354 0.96116139] mean value: 0.9483785822021116 key: test_jcc value: [0.42857143 0.45454545 0.42857143 0.375 0.33333333 0.42857143 0.33333333 0.71428571 0.57142857 0.71428571] mean value: 0.47819264069264067 key: train_jcc value: [0.88461538 0.88461538 0.92307692 0.92307692 0.88888889 0.85454545 0.88888889 0.94230769 0.88679245 0.9245283 ] mean value: 0.9001336294732522 MCC on Blind test: 0.15 MCC on Training: 0.31 Running classifier: 20 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.04428172 0.05332112 0.06045175 0.04622221 0.04114437 0.02618098 0.03252959 0.02668715 0.03240609 0.02707767] mean value: 0.039030265808105466 key: score_time value: [0.02660489 0.01513672 0.01660252 0.02446389 0.02242899 0.01183081 0.01920247 0.02199864 0.02013755 0.01156378] mean value: 0.01899702548980713 key: test_mcc value: [ 0.35355339 0.16903085 0.33333333 -0.16903085 -0.1 0.26666667 -0.1 0.69006556 0.3105295 0.26666667] mean value: 0.20208151183063544 key: train_mcc value: [0.80454045 0.82751593 0.80454045 0.82368777 0.86420225 0.82534898 0.86420225 0.78649572 0.76763491 0.82534898] mean value: 0.8193517679443023 key: test_fscore value: [0.6 0.61538462 0.66666667 0.36363636 0.4 0.6 0.4 0.8 0.6 0.66666667] mean value: 0.5712354312354313 key: train_fscore value: [0.9 0.90721649 0.90384615 0.91089109 0.93333333 0.91428571 0.93333333 0.89108911 0.88461538 0.91089109] mean value: 0.9089501701387995 key: test_precision value: [0.75 0.57142857 0.66666667 0.4 0.4 0.6 0.4 1. 0.75 0.66666667] mean value: 0.6204761904761905 key: train_precision value: [0.91836735 0.95652174 0.88679245 0.92 0.9245283 0.90566038 0.9245283 0.9 0.86792453 0.92 ] mean value: 0.912432304833336 key: test_recall value: [0.5 0.66666667 0.66666667 0.33333333 0.4 0.6 0.4 0.66666667 0.5 0.66666667] mean value: 0.5399999999999999 key: train_recall value: [0.88235294 0.8627451 0.92156863 0.90196078 0.94230769 0.92307692 0.94230769 0.88235294 0.90196078 0.90196078] mean value: 0.9062594268476621 key: test_accuracy value: [0.66666667 0.58333333 0.66666667 0.41666667 0.45454545 0.63636364 0.45454545 0.81818182 0.63636364 0.63636364] mean value: 0.5969696969696969 key: train_accuracy value: [0.90196078 0.91176471 0.90196078 0.91176471 0.93203883 0.91262136 0.93203883 0.89320388 0.88349515 0.91262136] mean value: 0.9093470397867887 key: test_roc_auc value: [0.66666667 0.58333333 0.66666667 0.41666667 0.45 0.63333333 0.45 0.83333333 0.65 0.63333333] mean value: 0.5983333333333334 key: train_roc_auc value: [0.90196078 0.91176471 0.90196078 0.91176471 0.93193816 0.91251885 0.93193816 0.89309955 0.8836727 0.91251885] mean value: 0.9093137254901962 key: test_jcc value: [0.42857143 0.44444444 0.5 0.22222222 0.25 0.42857143 0.25 0.66666667 0.42857143 0.5 ] mean value: 0.4119047619047619 key: train_jcc value: [0.81818182 0.83018868 0.8245614 0.83636364 0.875 0.84210526 0.875 0.80357143 0.79310345 0.83636364] mean value: 0.8334439313668331 MCC on Blind test: 0.02 MCC on Training: 0.2 Running classifier: 21 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=3))]) key: fit_time value: [0.03934979 0.04801559 0.05670094 0.03842807 0.03893948 0.0658505 0.08511567 0.07609868 0.09322 0.07130837] mean value: 0.061302709579467776 key: score_time value: [0.01236033 0.01214266 0.01223111 0.01214576 0.01220489 0.0250895 0.01219964 0.01492572 0.01231861 0.01216245] mean value: 0.013778066635131836 key: test_mcc value: [ 0.19245009 0.16903085 0. -0.16903085 -0.26666667 0.63333333 0.44854261 0.46666667 0.55901699 0.26666667] mean value: 0.2300009697677353 key: train_mcc value: [0.70710678 0.82751593 0.62853936 0.82368777 0.65045249 0.65107511 0.61222185 0.65081204 0.63189883 0.73013392] mean value: 0.6913444076800561 key: test_fscore value: [0.44444444 0.61538462 0.5 0.36363636 0.36363636 0.8 0.66666667 0.72727273 0.66666667 0.66666667] mean value: 0.5814374514374514 key: train_fscore value: [0.84848485 0.90721649 0.80808081 0.91089109 0.82692308 0.82352941 0.80392157 0.82 0.80808081 0.85714286] mean value: 0.8414270963058827 key: test_precision value: [0.66666667 0.57142857 0.5 0.4 0.33333333 0.8 0.75 0.8 1. 0.66666667] mean value: 0.6488095238095238 key: train_precision value: [0.875 0.95652174 0.83333333 0.92 0.82692308 0.84 0.82 0.83673469 0.83333333 0.89361702] mean value: 0.8635463197874327 key: test_recall value: [0.33333333 0.66666667 0.5 0.33333333 0.4 0.8 0.6 0.66666667 0.5 0.66666667] mean value: 0.5466666666666666 key: train_recall value: [0.82352941 0.8627451 0.78431373 0.90196078 0.82692308 0.80769231 0.78846154 0.80392157 0.78431373 0.82352941] mean value: 0.8207390648567119 key: test_accuracy value: [0.58333333 0.58333333 0.5 0.41666667 0.36363636 0.81818182 0.72727273 0.72727273 0.72727273 0.63636364] mean value: 0.6083333333333335 key: train_accuracy value: [0.85294118 0.91176471 0.81372549 0.91176471 0.82524272 0.82524272 0.80582524 0.82524272 0.81553398 0.86407767] mean value: 0.845136112697506 key: test_roc_auc value: [0.58333333 0.58333333 0.5 0.41666667 0.36666667 0.81666667 0.71666667 0.73333333 0.75 0.63333333] mean value: 0.61 key: train_roc_auc value: [0.85294118 0.91176471 0.81372549 0.91176471 0.82522624 0.82541478 0.80599548 0.82503771 0.81523379 0.86368778] mean value: 0.8450791855203619 key: test_jcc value: [0.28571429 0.44444444 0.33333333 0.22222222 0.22222222 0.66666667 0.5 0.57142857 0.5 0.5 ] mean value: 0.4246031746031746 key: train_jcc value: [0.73684211 0.83018868 0.6779661 0.83636364 0.70491803 0.7 0.67213115 0.69491525 0.6779661 0.75 ] mean value: 0.7281291058827064 MCC on Blind test: 0.23 MCC on Training: 0.23 Running classifier: 22 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.01284027 0.01238203 0.01115441 0.01097059 0.010221 0.01062846 0.01018357 0.01058888 0.01076984 0.01057148] mean value: 0.011031055450439453 key: score_time value: [0.01212358 0.00972319 0.01004553 0.0097568 0.00951314 0.00889063 0.00994396 0.00952625 0.00972319 0.00981116] mean value: 0.009905743598937988 key: test_mcc value: [ 0.16903085 -0.35355339 0.33333333 0. -0.1 0.26666667 -0.3105295 0.26666667 0.1490712 0.06900656] mean value: 0.04896923797492582 key: train_mcc value: [0.68680282 0.7254902 0.7254902 0.64705882 0.63122172 0.67119711 0.67119711 0.63122172 0.64029486 0.73282456] mean value: 0.6762799109293481 key: test_fscore value: [0.54545455 0.42857143 0.66666667 0.5 0.4 0.6 0.22222222 0.66666667 0.44444444 0.61538462] mean value: 0.5089410589410589 key: train_fscore value: [0.84 0.8627451 0.8627451 0.82352941 0.81553398 0.83168317 0.83168317 0.81553398 0.79569892 0.85416667] mean value: 0.8333319497039697 key: test_precision value: [0.6 0.375 0.66666667 0.5 0.4 0.6 0.25 0.66666667 0.66666667 0.57142857] mean value: 0.5296428571428572 key: train_precision value: [0.85714286 0.8627451 0.8627451 0.82352941 0.82352941 0.85714286 0.85714286 0.80769231 0.88095238 0.91111111] mean value: 0.8543733390792214 key: test_recall value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) [0.5 0.5 0.66666667 0.5 0.4 0.6 0.2 0.66666667 0.33333333 0.66666667] mean value: 0.5033333333333333 key: train_recall value: [0.82352941 0.8627451 0.8627451 0.82352941 0.80769231 0.80769231 0.80769231 0.82352941 0.7254902 0.80392157] mean value: 0.8148567119155354 key: test_accuracy value: [0.58333333 0.33333333 0.66666667 0.5 0.45454545 0.63636364 0.36363636 0.63636364 0.54545455 0.54545455] mean value: 0.5265151515151514 key: train_accuracy value: [0.84313725 0.8627451 0.8627451 0.82352941 0.81553398 0.83495146 0.83495146 0.81553398 0.81553398 0.86407767] mean value: 0.8372739387016942 key: test_roc_auc value: [0.58333333 0.33333333 0.66666667 0.5 0.45 0.63333333 0.35 0.63333333 0.56666667 0.53333333] mean value: 0.525 key: train_roc_auc value: [0.84313725 0.8627451 0.8627451 0.82352941 0.81561086 0.8352187 0.8352187 0.81561086 0.81466817 0.86349925] mean value: 0.8371983408748115 key: test_jcc value: [0.375 0.27272727 0.5 0.33333333 0.25 0.42857143 0.125 0.5 0.28571429 0.44444444] mean value: 0.35147907647907645 key: train_jcc value: [0.72413793 0.75862069 0.75862069 0.7 0.68852459 0.71186441 0.71186441 0.68852459 0.66071429 0.74545455] mean value: 0.7148326135400849 MCC on Blind test: 0.15 MCC on Training: 0.05 Running classifier: 23 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.01303983 0.0118289 0.01300478 0.01349401 0.01254606 0.01221347 0.01395392 0.01300001 0.0125289 0.0126524 ] mean value: 0.012826228141784668 key: score_time value: [0.00956917 0.01069283 0.01084113 0.01126218 0.01142693 0.01148152 0.01140547 0.01129746 0.01173377 0.01150513] mean value: 0.011121559143066406 key: test_mcc value: [ 0.30151134 0. -0.19245009 0.19245009 0.04303315 0.51639778 -0.1 0.1490712 0. 0.1 ] mean value: 0.10100134708632655 key: train_mcc value: [0.61512469 0.14142136 0.61512469 0.4152274 0.48613777 0.47099964 0.76763491 0.66245123 0.27266537 0.48023493] mean value: 0.4927021979950645 key: test_fscore value: [0.28571429 0.66666667 0.53333333 0.44444444 0.28571429 0.57142857 0.4 0.44444444 0. 0.54545455] mean value: 0.4177200577200577 key: train_fscore value: [0.70886076 0.67549669 0.816 0.45454545 0.55555556 0.53521127 0.88235294 0.80434783 0.24137931 0.54285714] mean value: 0.6216606946407434 key: test_precision value: [1. 0.5 0.44444444 0.66666667 0.5 1. 0.4 0.66666667 0. 0.6 ] mean value: 0.5777777777777777 key: train_precision value: [1. 0.51 0.68918919 1. 1. 1. 0.9 0.90243902 1. 1. ] mean value: 0.9001628213579433 key: test_recall value: [0.16666667 1. 0.66666667 0.33333333 0.2 0.4 0.4 0.33333333 0. 0.5 ] mean value: 0.4 key: train_recall value: [0.54901961 1. 1. 0.29411765 0.38461538 0.36538462 0.86538462 0.7254902 0.1372549 0.37254902] mean value: 0.5693815987933636 key: test_accuracy value: [0.58333333 0.5 0.41666667 0.58333333 0.54545455 0.72727273 0.45454545 0.54545455 0.45454545 0.54545455] mean value: 0.5356060606060605 key: train_accuracy value: [0.7745098 0.51960784 0.7745098 0.64705882 0.68932039 0.67961165 0.88349515 0.82524272 0.57281553 0.68932039] mean value: 0.7055492099752523 key: test_roc_auc value: [0.58333333 0.5 0.41666667 0.58333333 0.51666667 0.7 0.45 0.56666667 0.5 0.55 ] mean value: 0.5366666666666666 key: train_roc_auc value: [0.7745098 0.51960784 0.7745098 0.64705882 0.69230769 0.68269231 0.8836727 0.82428356 0.56862745 0.68627451] mean value: 0.7053544494720966 key: test_jcc value: [0.16666667 0.5 0.36363636 0.28571429 0.16666667 0.4 0.25 0.28571429 0. 0.375 ] mean value: 0.27933982683982683 key: train_jcc value: [0.54901961 0.51 0.68918919 0.29411765 0.38461538 0.36538462 0.78947368 0.67272727 0.1372549 0.37254902] mean value: 0.4764331322597576 MCC on Blind test: -0.22 MCC on Training: 0.1 Running classifier: 24 Model_name: XGBoost Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.07257295 0.0562458 0.04685879 0.0469768 0.0518899 0.05227637 0.05125976 0.0498898 0.04955792 0.05208349] mean value: 0.05296115875244141 key: score_time value: [0.01064181 0.01110029 0.01014471 0.01063538 0.01027966 0.0107975 0.01079798 0.01054859 0.01006508 0.01006055] mean value: 0.010507154464721679 key: test_mcc value: [0.4472136 0.35355339 0.16903085 0.33333333 0.1490712 0.3105295 0.2608746 0.3105295 0.69006556 0.63333333] mean value: 0.36575348623310366 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.5 0.71428571 0.54545455 0.66666667 0.61538462 0.66666667 0.5 0.6 0.8 0.83333333] mean value: 0.6441791541791542 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 0.625 0.6 0.66666667 0.5 0.57142857 0.66666667 0.75 1. 0.83333333] /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:463: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_CV['source_data'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:490: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_BT['source_data'] = 'BT' mean value: 0.7213095238095237 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.33333333 0.83333333 0.5 0.66666667 0.8 0.8 0.4 0.5 0.66666667 0.83333333] mean value: 0.6333333333333333 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.66666667 0.66666667 0.58333333 0.66666667 0.54545455 0.63636364 0.63636364 0.63636364 0.81818182 0.81818182] mean value: 0.6674242424242424 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.66666667 0.66666667 0.58333333 0.66666667 0.56666667 0.65 0.61666667 0.65 0.83333333 0.81666667] mean value: 0.6716666666666666 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.33333333 0.55555556 0.375 0.5 0.44444444 0.5 0.33333333 0.42857143 0.66666667 0.71428571] mean value: 0.4851190476190476 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.27 MCC on Training: 0.37 Extracting tts_split_name: sl Total cols in each df: CV df: 8 metaDF: 17 Adding column: Model_name Total cols in bts df: BT_df: 8 First proceeding to rowbind CV and BT dfs: Final output should have: 25 columns Combinig 2 using pd.concat by row ~ rowbind Checking Dims of df to combine: Dim of CV: (24, 8) Dim of BT: (24, 8) 8 Number of Common columns: 8 These are: ['source_data', 'Precision', 'JCC', 'MCC', 'Recall', 'Accuracy', 'F1', 'ROC_AUC'] Concatenating dfs with different resampling methods [WF]: Split type: sl No. of dfs combining: 2 PASS: 2 dfs successfully combined nrows in combined_df_wf: 48 ncols in combined_df_wf: 8 PASS: proceeding to merge metadata with CV and BT dfs Adding column: Model_name ========================================================= SUCCESS: Ran multiple classifiers ======================================================= ============================================================== Running several classification models (n): 24 List of models: ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('Gaussian NB', GaussianNB()) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('K-Nearest Neighbors', KNeighborsClassifier()) ('LDA', LinearDiscriminantAnalysis()) ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(cv=3, random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Multinomial', MultinomialNB()) ('Naive Bayes', BernoulliNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=12, random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Random Forest', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42)) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=3)) ('SVC', SVC(random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=12, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ================================================================ Running classifier: 1 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.15796471 0.15983391 0.16137171 0.16179204 0.16256452 0.16152716 0.15678501 0.14991021 0.1620996 0.16241646] mean value: 0.15962653160095214 key: score_time value: [0.01554084 0.01638031 0.01637244 0.01642966 0.01629543 0.0165112 0.01481986 0.01627636 0.01626348 0.01626277] mean value: 0.016115236282348632 key: test_mcc value: [0.73692303 0.84254172 0.84254172 0.67037015 0.84147165 0.87917396 0.76896316 0.95825929 0.87705802 0.69560834] mean value: 0.8112911043178673 key: train_mcc value: [0.97188929 0.97652111 0.95811639 0.96268722 0.96238859 0.97154077 0.95777743 0.97652373 0.94017476 0.95761488] mean value: 0.9635234159551977 key: test_fscore value: [0.86792453 0.92 0.92 0.84 0.92307692 0.94117647 0.88888889 0.97959184 0.93877551 0.85185185] mean value: 0.9071286009646562 key: train_fscore value: [0.98598131 0.9882904 0.97911833 0.98139535 0.98122066 0.98578199 0.97892272 0.98823529 0.97011494 0.97892272] mean value: 0.981798370254122 key: test_precision value: [0.76666667 0.85185185 0.85185185 0.77777778 0.85714286 0.88888889 0.8 0.96 0.88461538 0.74193548] mean value: 0.8380730762666246 key: train_precision value: [0.97235023 0.97685185 0.95909091 0.96347032 0.96759259 0.98113208 0.96313364 0.97674419 0.94196429 0.96759259] mean value: 0.9669922683962888 key: test_recall value: [1. 1. 1. 0.91304348 1. 1. 1. 1. 1. 1. ] mean value: 0.9913043478260869 key: train_recall value: [1. 1. 1. 1. 0.9952381 0.99047619 0.9952381 1. 1. 0.99052133] mean value: 0.9971473707966598 key: test_accuracy value: [0.85106383 0.91489362 0.91489362 0.82978723 0.91489362 0.93617021 0.87234043 0.9787234 0.93478261 0.82608696] mean value: 0.8973635522664198 key: train_accuracy value: [0.98574822 0.98812352 0.97862233 0.98099762 0.98099762 0.98574822 0.97862233 0.98812352 0.96919431 0.97867299] mean value: 0.9814850671499814 key: test_roc_auc value: [0.85416667 0.91666667 0.91666667 0.83152174 0.91304348 0.93478261 0.86956522 0.97826087 0.93478261 0.82608696] mean value: 0.8975543478260869 key: train_roc_auc value: [0.98571429 0.98809524 0.97857143 0.98095238 0.98103137 0.98575942 0.9786617 0.98815166 0.96919431 0.97867299] mean value: 0.9814804784473031 key: test_jcc value: [0.76666667 0.85185185 0.85185185 0.72413793 0.85714286 0.88888889 0.8 0.96 0.88461538 0.74193548] mean value: 0.8327090915922952 key: train_jcc value: [0.97235023 0.97685185 0.95909091 0.96347032 0.96313364 0.97196262 0.9587156 0.97674419 0.94196429 0.9587156 ] mean value: [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... 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Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. 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[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... ð?•G”Œnumpy”Œdtype”“”Œf8”‰ˆ‡”R”(KŒ<”NNNJÿÿÿÿJÿÿÿÿKt”bM¥K´†”ŒC”t”R”.•—”…”R”Kt”}”‡”ahŒThreadingBackend”“”)”}”(Œ nesting_level”KŒinner_max_num_threads”NubN†”N}”t”R”sbŒargs”)Œkwargs”}”Œ loky_pickler”Œ cloudpickle”uBuilding estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Œ loky_pqà*ÉçU@Œ“þ~[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.2s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.9s remaining: 1.9s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.0s remaining: 0.3s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.0s remaining: 2.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.1s remaining: 2.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.1s remaining: 2.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.1s remaining: 0.4s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.1s remaining: 2.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.1s remaining: 2.2s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.1s remaining: 2.2s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.1s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.1s remaining: 2.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.1s remaining: 0.4s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.1s remaining: 0.4s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.1s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.1s remaining: 0.4s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.1s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.1s remaining: 0.4s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.1s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.2s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.1s remaining: 2.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.2s remaining: 0.4s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.2s remaining: 0.4s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.2s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.2s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.2s remaining: 0.4s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 1.2s remaining: 2.5s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.2s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 1.2s remaining: 0.4s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.3s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... 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Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.2s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished 0.9642999232788985 MCC on Blind test: -0.06 MCC on Training: 0.81 Running classifier: 2 Model_name: Bagging Classifier Model func: BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3))]) key: fit_time value: [0.20522928 0.19652057 0.22537565 0.23216295 0.21626139 0.22686052 0.22213769 0.24588108 0.22548842 0.23701072] mean value: 0.22329282760620117 key: score_time value: [0.05409884 0.04757714 0.03809094 0.06493139 0.04725146 0.0363512 0.07359505 0.05120015 0.06289649 0.08589935] mean value: 0.0561892032623291 key: test_mcc value: [0.91833182 0.95833333 0.95833333 0.91485507 0.91804649 0.76896316 0.91804649 0.91804649 0.91651514 0.91651514] mean value: 0.9105986483221633 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.95833333 0.9787234 0.9787234 0.95652174 0.96 0.88888889 0.96 0.96 0.95833333 0.95833333] mean value: 0.9557857436529963 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.92 0.95833333 0.95833333 0.95652174 0.92307692 0.8 0.92307692 0.92307692 0.92 0.92 ] mean value: 0.920241917502787 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 0.95652174 1. 1. 1. 1. 1. 1. ] mean value: 0.9956521739130434 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.95744681 0.9787234 0.9787234 0.95744681 0.95744681 0.87234043 0.95744681 0.95744681 0.95652174 0.95652174] mean value: 0.9530064754856615 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.95833333 0.97916667 0.97916667 0.95742754 0.95652174 0.86956522 0.95652174 0.95652174 0.95652174 0.95652174] mean value: 0.9526268115942031 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.92 0.95833333 0.95833333 0.91666667 0.92307692 0.8 0.92307692 0.92307692 0.92 0.92 ] mean value: 0.9162564102564102 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.12 MCC on Training: 0.91 Running classifier: 3 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.02296281 0.02370477 0.02193499 0.02189922 0.02303267 0.0211308 0.02398157 0.02302003 0.02496672 0.02469349] mean value: 0.023132705688476564 key: score_time value: [0.01005363 0.00905132 0.00994921 0.00935602 0.00920892 0.00966692 0.0093565 0.00877285 0.0100913 0.00948334] mean value: 0.009499001502990722 key: test_mcc value: [0.77125066 0.91833182 0.87979456 0.62296012 0.95825929 0.66534784 0.95825929 0.76896316 0.91651514 0.73029674] mean value: 0.8189978630170964 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.88461538 0.95833333 0.93877551 0.81632653 0.97959184 0.84210526 0.97959184 0.88888889 0.95833333 0.86792453] mean value: 0.9114486445916435 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.79310345 0.92 0.88461538 0.76923077 0.96 0.72727273 0.96 0.8 0.92 0.76666667] mean value: 0.8500888996061411 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 0.86956522 1. 1. 1. 1. 1. 1. ] mean value: 0.9869565217391305 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.87234043 0.95744681 0.93617021 0.80851064 0.9787234 0.80851064 0.9787234 0.87234043 0.95652174 0.84782609] mean value: 0.9017113783533766 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.875 0.95833333 0.9375 0.80978261 0.97826087 0.80434783 0.97826087 0.86956522 0.95652174 0.84782609] mean value: 0.9015398550724637 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.79310345 0.92 0.88461538 0.68965517 0.96 0.72727273 0.96 0.8 0.92 0.76666667] mean value: 0.8421313399244434 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.12 MCC on Training: 0.82 Running classifier: 4 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.01142049 0.01150918 0.01079082 0.01056504 0.01079154 0.01128221 0.01168442 0.011446 0.01138306 0.01143003] mean value: 0.011230278015136718 key: score_time value: [0.00949669 0.00899076 0.00933838 0.00910282 0.00932693 0.01004267 0.00993466 0.00993061 0.00974631 0.00985813] mean value: 0.009576797485351562 key: test_mcc value: [0.77125066 0.84254172 0.91833182 0.82971014 0.76896316 0.8047833 0.91804649 0.8047833 0.76564149 0.95742711] mean value: 0.8381479207040444 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.88461538 0.92 0.95833333 0.91304348 0.88888889 0.90566038 0.96 0.90566038 0.88461538 0.9787234 ] mean value: 0.9199540628686161 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.79310345 0.85185185 0.92 0.91304348 0.8 0.82758621 0.92307692 0.82758621 0.79310345 0.95833333] mean value: 0.8607684896867805 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 0.91304348 1. 1. 1. 1. 1. 1. ] mean value: 0.9913043478260869 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.87234043 0.91489362 0.95744681 0.91489362 0.87234043 0.89361702 0.95744681 0.89361702 0.86956522 0.97826087] mean value: 0.9124421831637373 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.875 0.91666667 0.95833333 0.91485507 0.86956522 0.89130435 0.95652174 0.89130435 0.86956522 0.97826087] mean value: 0.9121376811594203 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.79310345 0.85185185 0.92 0.84 0.8 0.82758621 0.92307692 0.82758621 0.79310345 0.95833333] mean value: 0.8534641418606936 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.49 MCC on Training: 0.84 Running classifier: 5 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.11966443 0.12827754 0.12908363 0.12613606 0.12604356 0.12585807 0.12560368 0.12437797 0.11957979 0.1182456 ] mean value: 0.12428703308105468 key: score_time value: [0.01948261 0.01950383 0.019243 0.01995969 0.01964808 0.01966214 0.01798677 0.01917267 0.01944542 0.01779437] mean value: 0.019189858436584474 key: test_mcc value: [0.91833182 0.95833333 1. 0.91804649 0.87917396 0.87917396 0.91804649 0.87917396 0.95742711 1. ] mean value: 0.9307707142952705 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.95833333 0.9787234 1. 0.95454545 0.94117647 0.94117647 0.96 0.94117647 0.9787234 1. ] mean value: 0.9653855008154132 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.92 0.95833333 1. 1. 0.88888889 0.88888889 0.92307692 0.88888889 0.95833333 1. ] mean value: 0.9426410256410257 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 0.91304348 1. 1. 1. 1. 1. 1. ] mean value: 0.9913043478260869 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.95744681 0.9787234 1. 0.95744681 0.93617021 0.93617021 0.95744681 0.93617021 0.97826087 1. ] mean value: 0.9637835337650325 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.95833333 0.97916667 1. 0.95652174 0.93478261 0.93478261 0.95652174 0.93478261 0.97826087 1. ] mean value: 0.9633152173913043 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.92 0.95833333 1. 0.91304348 0.88888889 0.88888889 0.92307692 0.88888889 0.95833333 1. ] mean value: 0.9339453734671126 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.05 MCC on Training: 0.93 Running classifier: 6 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.5654285 0.56222057 0.57922935 0.57928419 0.58483791 0.56946039 0.58107495 0.56714296 0.55630517 0.55692482] mean value: 0.5701908826828003 key: score_time value: [0.00924683 0.00926733 0.01039791 0.01020646 0.01004601 0.00909257 0.0093224 0.00980306 0.00919771 0.0099895 ] mean value: 0.009656977653503419 key: test_mcc value: [0.91833182 0.91833182 0.87979456 0.78804348 0.8047833 0.8047833 0.91804649 0.87917396 0.87705802 0.87705802] mean value: 0.8665404775812101 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.99527184] mean value: 0.9995271841124749 key: test_fscore value: [0.95833333 0.95833333 0.93877551 0.89361702 0.90566038 0.90566038 0.96 0.94117647 0.93877551 0.93877551] mean value: 0.9339107443860722 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.9976247] mean value: 0.9997624703087886 key: test_precision value: [0.92 0.92 0.88461538 0.875 0.82758621 0.82758621 0.92307692 0.88888889 0.88461538 0.88461538] mean value: 0.8835984379605071 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 0.91304348 1. 1. 1. 1. 1. 1. ] mean value: 0.9913043478260869 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.99526066] mean value: 0.9995260663507108 key: test_accuracy value: [0.95744681 0.95744681 0.93617021 0.89361702 0.89361702 0.89361702 0.95744681 0.93617021 0.93478261 0.93478261] mean value: 0.9295097132284921 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.99763033] mean value: 0.9997630331753555 key: test_roc_auc value: [0.95833333 0.95833333 0.9375 0.89402174 0.89130435 0.89130435 0.95652174 0.93478261 0.93478261 0.93478261] mean value: 0.9291666666666666 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.99763033] mean value: 0.9997630331753555 key: test_jcc value: [0.92 0.92 0.88461538 0.80769231 0.82758621 0.82758621 0.92307692 0.88888889 0.88461538 0.88461538] mean value: 0.8768676687297378 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 0.99526066] mean value: 0.9995260663507108 MCC on Blind test: 0.22 MCC on Training: 0.87 Running classifier: 7 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.01024485 0.01273656 0.010746 0.01154542 0.0104599 0.0101707 0.0106504 0.01059294 0.01041484 0.01081347] mean value: 0.010837507247924805 key: score_time value: [0.00952268 0.00943589 0.00946546 0.00941396 0.0094564 0.00945902 0.00932217 0.00932074 0.00899005 0.00955725] mean value: 0.009394359588623048 key: test_mcc value: [ 0.27586252 0.33414756 0.32605546 0.40437762 0.28051421 0.0282707 -0.0634058 0.32602701 0.22075539 0.26111648] mean value: 0.2393721163580999 key: train_mcc value: [0.43587952 0.38204908 0.45886946 0.45706583 0.36817874 0.35337938 0.33576471 0.35786377 0.40329679 0.44731486] mean value: 0.3999662140644049 key: test_fscore value: [0.62222222 0.57894737 0.6 0.68181818 0.62222222 0.43902439 0.46808511 0.63636364 0.57142857 0.62222222] mean value: 0.584233392132499 key: train_fscore value: [0.70761671 0.66496164 0.70588235 0.71032746 0.68408551 0.62903226 0.65346535 0.63270777 0.69417476 0.70967742] mean value: 0.6791931220029249 key: test_precision value: [0.63636364 0.73333333 0.70588235 0.71428571 0.66666667 0.52941176 0.47826087 0.7 0.63157895 0.63636364] mean value: 0.6432146921593684 key: train_precision value: [0.73469388 0.72222222 0.76666667 0.75806452 0.68246445 0.72222222 0.68041237 0.72392638 0.71144279 0.74479167] mean value: 0.7246907164005905 key: test_recall value: [0.60869565 0.47826087 0.52173913 0.65217391 0.58333333 0.375 0.45833333 0.58333333 0.52173913 0.60869565] mean value: 0.5391304347826086 key: train_recall value: [0.68246445 0.61611374 0.65402844 0.66824645 0.68571429 0.55714286 0.62857143 0.56190476 0.67772512 0.67772512] mean value: 0.6409636650868877 key: test_accuracy value: [0.63829787 0.65957447 0.65957447 0.70212766 0.63829787 0.5106383 0.46808511 0.65957447 0.60869565 0.63043478] mean value: 0.6175300647548566 key: train_accuracy value: [0.71733967 0.6888361 0.72684086 0.72684086 0.68408551 0.67220903 0.66745843 0.67458432 0.7014218 0.72274882] mean value: 0.6982365390460537 key: test_roc_auc value: [0.63768116 0.6557971 0.6567029 0.70108696 0.63949275 0.51358696 0.4682971 0.66123188 0.60869565 0.63043478] mean value: 0.6173007246376812 key: train_roc_auc value: [0.7174227 0.68900925 0.72701422 0.72698037 0.68408937 0.67193636 0.66736628 0.67431731 0.7014218 0.72274882] mean value: 0.6982306477093208 key: test_jcc value: [0.4516129 0.40740741 0.42857143 0.51724138 0.4516129 0.28125 0.30555556 0.46666667 0.4 0.4516129 ] mean value: 0.4161531147188822 key: train_jcc value: [0.54752852 0.49808429 0.54545455 0.55078125 0.5198556 0.45882353 0.48529412 0.4627451 0.53159851 0.55 ] mean value: 0.5150165457529613 MCC on Blind test: -0.06 MCC on Training: 0.24 Running classifier: 8 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.12026477 0.16222358 0.15195417 0.18023658 0.15140486 0.14792776 0.14441776 0.14493656 0.14056945 0.14182758] mean value: 0.14857630729675292 key: score_time value: [0.02337527 0.03727126 0.03483748 0.03461099 0.03467917 0.03472066 0.03383422 0.03448677 0.03436208 0.03356314] mean value: 0.03357410430908203 key: test_mcc value: [0.55975995 0.87318841 0.87318841 0.7085716 0.76896316 0.68038162 0.8047833 0.73387289 0.91651514 0.82922798] mean value: 0.7748452465985431 key: train_mcc value: [0.92502614 0.92548525 0.93453919 0.92156936 0.94406465 0.93455374 0.93953298 0.93502048 0.93432109 0.93432109] mean value: 0.9328433960346343 key: test_fscore value: [0.79245283 0.93617021 0.93617021 0.85714286 0.88888889 0.85185185 0.90566038 0.87272727 0.95833333 0.91666667] mean value: 0.8916064503689956 key: train_fscore value: [0.96296296 0.96313364 0.96759259 0.9610984 0.97209302 0.96744186 0.96983759 0.96759259 0.96744186 0.96744186] mean value: 0.9666636378528605 key: test_precision value: [0.7 0.91666667 0.91666667 0.80769231 0.8 0.76666667 0.82758621 0.77419355 0.92 0.88 ] mean value: 0.8309472062975957 key: train_precision value: [0.94117647 0.93721973 0.94570136 0.92920354 0.95 0.94545455 0.94570136 0.94144144 0.94977169 0.94977169] mean value: 0.9435441822176497 key: test_recall value: [0.91304348 0.95652174 0.95652174 0.91304348 1. 0.95833333 1. 1. 1. 0.95652174] mean value: 0.9653985507246376 key: train_recall value: [0.98578199 0.99052133 0.99052133 0.99526066 0.9952381 0.99047619 0.9952381 0.9952381 0.98578199 0.98578199] mean value: 0.9909839765290002 key: test_accuracy value: [0.76595745 0.93617021 0.93617021 0.85106383 0.87234043 0.82978723 0.89361702 0.85106383 0.95652174 0.91304348] mean value: 0.8805735430157261 key: train_accuracy value: [0.96199525 0.96199525 0.96674584 0.95961995 0.97149644 0.96674584 0.96912114 0.96674584 0.96682464 0.96682464] mean value: 0.9658114847294301 key: test_roc_auc value: [0.76902174 0.9365942 0.9365942 0.85235507 0.86956522 0.82699275 0.89130435 0.84782609 0.95652174 0.91304348] mean value: 0.8799818840579711 key: train_roc_auc value: [0.96193861 0.96192733 0.96668923 0.95953509 0.9715527 0.96680208 0.96918303 0.96681336 0.96682464 0.96682464] mean value: 0.9658090724441436 key: test_jcc value: [0.65625 0.88 0.88 0.75 0.8 0.74193548 0.82758621 0.77419355 0.92 0.84615385] mean value: 0.8076119085308463 key: train_jcc value: [0.92857143 0.92888889 0.93721973 0.92511013 0.94570136 0.93693694 0.94144144 0.93721973 0.93693694 0.93693694] mean value: 0.9354963521220631 MCC on Blind test: 0.05 MCC on Training: 0.77 Running classifier: 9 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.01263118 0.01100016 0.01108098 0.01024866 0.00988173 0.01026726 0.01036167 0.0105567 0.00981212 0.00871015] mean value: 0.010455060005187988 key: score_time value: [0.05205822 0.01363754 0.01747775 0.01735806 0.01793122 0.01442313 0.0133636 0.0158093 0.01589036 0.01688242] mean value: 0.019483160972595216 key: test_mcc value: [0.31884058 0.54621844 0.32123465 0.31987214 0.50052164 0.54211097 0.59180008 0.36699609 0.6092718 0.65465367] mean value: 0.4771520055637287 key: train_mcc value: [0.66580774 0.69268031 0.67549916 0.70208694 0.71037448 0.70068306 0.70770772 0.70226306 0.69853547 0.69572524] mean value: 0.6951363182615398 key: test_fscore value: [0.65217391 0.78431373 0.66666667 0.70175439 0.77966102 0.79245283 0.81481481 0.71698113 0.80851064 0.83333333] mean value: 0.7550662456824577 key: train_fscore value: [0.84233261 0.85412262 0.84665227 0.85774059 0.86147186 0.85714286 0.86026201 0.85776805 0.85653105 0.85529158] mean value: 0.8549315494337199 key: test_precision value: [0.65217391 0.71428571 0.64 0.58823529 0.65714286 0.72413793 0.73333333 0.65517241 0.79166667 0.8 ] mean value: 0.6956148123417283 key: train_precision value: [0.77380952 0.77099237 0.77777778 0.76779026 0.78968254 0.78571429 0.79435484 0.79352227 0.78125 0.78571429] mean value: 0.7820608147199066 key: test_recall value: [0.65217391 0.86956522 0.69565217 0.86956522 0.95833333 0.875 0.91666667 0.79166667 0.82608696 0.86956522] mean value: 0.832427536231884 key: train_recall value: [0.92417062 0.95734597 0.92890995 0.97156398 0.94761905 0.94285714 0.93809524 0.93333333 0.9478673 0.93838863] mean value: 0.9430151207402393 key: test_accuracy value: [0.65957447 0.76595745 0.65957447 0.63829787 0.72340426 0.76595745 0.78723404 0.68085106 0.80434783 0.82608696] mean value: 0.7311285846438482 key: train_accuracy value: [0.82660333 0.83610451 0.83135392 0.83847981 0.847981 0.8432304 0.847981 0.8456057 0.84123223 0.84123223] mean value: 0.8399804122434735 key: test_roc_auc value: [0.65942029 0.76811594 0.66032609 0.64311594 0.7182971 0.76358696 0.78442029 0.67844203 0.80434783 0.82608696] mean value: 0.7306159420289856 key: train_roc_auc value: [0.82637102 0.83581584 0.83112164 0.83816294 0.84821711 0.84346649 0.84819454 0.84581359 0.84123223 0.84123223] mean value: 0.8399627623561272 key: test_jcc value: [0.48387097 0.64516129 0.5 0.54054054 0.63888889 0.65625 0.6875 0.55882353 0.67857143 0.71428571] mean value: 0.6103892359762854 key: train_jcc value: [0.72761194 0.74538745 0.7340824 0.75091575 0.75665399 0.75 0.75478927 0.75095785 0.74906367 0.74716981] mean value: 0.7466632142657501 MCC on Blind test: -0.15 MCC on Training: 0.48 Running classifier: 10 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.05166626 0.0590806 0.07293487 0.03803897 0.03851414 0.08580637 0.0777514 0.03941464 0.03927064 0.06563044] mean value: 0.056810832023620604 key: score_time value: [0.03252578 0.02317572 0.01206517 0.01201892 0.01203918 0.03430724 0.01214838 0.01209378 0.01243758 0.02084875] mean value: 0.018366050720214844 key: test_mcc value: [0.37275718 0.67037015 0.66243303 0.64834149 0.75474102 0.57227835 0.57227835 0.62966842 0.73029674 0.73029674] mean value: 0.6343461469387627 key: train_mcc value: [0.85936223 0.8048343 0.80848438 0.82394622 0.80056703 0.82696095 0.82696095 0.81810351 0.82733417 0.78709873] mean value: 0.8183652460332226 key: test_fscore value: [0.70588235 0.84 0.83333333 0.83018868 0.88461538 0.80701754 0.80701754 0.83018868 0.86792453 0.86792453] mean value: 0.8274092573703532 key: train_fscore value: [0.93119266 0.90540541 0.90702948 0.91441441 0.90293454 0.91533181 0.91533181 0.91116173 0.91571754 0.89686099] mean value: 0.9115380369252721 key: test_precision value: [0.64285714 0.77777778 0.8 0.73333333 0.82142857 0.6969697 0.6969697 0.75862069 0.76666667 0.76666667] mean value: 0.7461290242324725 key: train_precision value: [0.90222222 0.86266094 0.86956522 0.87124464 0.8583691 0.88105727 0.88105727 0.87336245 0.88157895 0.85106383] mean value: 0.8732181877740551 key: test_recall value: [0.7826087 0.91304348 0.86956522 0.95652174 0.95833333 0.95833333 0.95833333 0.91666667 1. 1. ] mean value: 0.931340579710145 key: train_recall value: [0.96208531 0.95260664 0.9478673 0.96208531 0.95238095 0.95238095 0.95238095 0.95238095 0.95260664 0.9478673 ] mean value: 0.9534642292936132 key: test_accuracy value: [0.68085106 0.82978723 0.82978723 0.80851064 0.87234043 0.76595745 0.76595745 0.80851064 0.84782609 0.84782609] mean value: 0.8057354301572618 key: train_accuracy value: [0.92874109 0.90023753 0.90261283 0.90973872 0.89786223 0.91211401 0.91211401 0.90736342 0.91232227 0.89099526] mean value: 0.9074101383526021 key: test_roc_auc value: [0.68297101 0.83152174 0.83061594 0.8115942 0.87047101 0.76177536 0.76177536 0.80615942 0.84782609 0.84782609] mean value: 0.8052536231884059 key: train_roc_auc value: [0.9286617 0.90011284 0.90250508 0.90961408 0.89799142 0.91220943 0.91220943 0.9074701 0.91232227 0.89099526] mean value: 0.9074091627172196 key: test_jcc value: [0.54545455 0.72413793 0.71428571 0.70967742 0.79310345 0.67647059 0.67647059 0.70967742 0.76666667 0.76666667] mean value: 0.7082610987564204 key: train_jcc value: [0.87124464 0.82716049 0.82987552 0.84232365 0.82304527 0.84388186 0.84388186 0.83682008 0.84453782 0.81300813] mean value: 0.8375779308604014 MCC on Blind test: -0.31 MCC on Training: 0.63 Running classifier: 11 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.04908299 0.03953362 0.03937364 0.03914976 0.0391686 0.05879307 0.05967402 0.05927896 0.06243443 0.03980947] mean value: 0.04862985610961914 key: score_time value: [0.01271582 0.01300192 0.01301098 0.01270556 0.01272702 0.01290607 0.01330137 0.01418829 0.01627755 0.01222754] mean value: 0.013306212425231934 key: test_mcc value: [0.32123465 0.65942029 0.65942029 0.4899891 0.58127976 0.20768533 0.45455353 0.48913043 0.43519414 0.52623481] mean value: 0.4824142331189318 key: train_mcc value: [0.72034376 0.68839462 0.71698624 0.7074557 0.66858858 0.67237841 0.72013375 0.71550531 0.66357418 0.73085166] mean value: 0.7004212210811751 key: test_fscore value: [0.66666667 0.82608696 0.82608696 0.72727273 0.80769231 0.6779661 0.75471698 0.75 0.71111111 0.7755102 ] mean value: 0.7523110012694915 key: train_fscore value: [0.86310905 0.84931507 0.8630137 0.85844749 0.83796296 0.83373494 0.8618267 0.85981308 0.83294118 0.86836028] mean value: 0.8528524442764928 key: test_precision value: [0.64 0.82608696 0.82608696 0.76190476 0.75 0.57142857 0.68965517 0.75 0.72727273 0.73076923] mean value: 0.7273204376832563 key: train_precision value: [0.84545455 0.81938326 0.83259912 0.82819383 0.81531532 0.84390244 0.84792627 0.8440367 0.8271028 0.84684685] mean value: 0.8350761126361972 key: test_recall value: [0.69565217 0.82608696 0.82608696 0.69565217 0.875 0.83333333 0.83333333 0.75 0.69565217 0.82608696] mean value: 0.7856884057971014 key: train_recall value: [0.88151659 0.88151659 0.8957346 0.89099526 0.86190476 0.82380952 0.87619048 0.87619048 0.83886256 0.89099526] mean value: 0.8717716091175808 key: test_accuracy value: [0.65957447 0.82978723 0.82978723 0.74468085 0.78723404 0.59574468 0.72340426 0.74468085 0.7173913 0.76086957] mean value: 0.7393154486586494 key: train_accuracy value: [0.85985748 0.8432304 0.85748219 0.85273159 0.83372922 0.83610451 0.85985748 0.85748219 0.83175355 0.86492891] mean value: 0.84971575238374 key: test_roc_auc value: [0.66032609 0.82971014 0.82971014 0.74365942 0.78532609 0.59057971 0.72101449 0.74456522 0.7173913 0.76086957] mean value: 0.7383152173913043 key: train_roc_auc value: [0.85980591 0.84313925 0.85739111 0.85264049 0.83379598 0.83607538 0.85989619 0.85752652 0.83175355 0.86492891] mean value: 0.8496953283683141 key: test_jcc value: [0.5 0.7037037 0.7037037 0.57142857 0.67741935 0.51282051 0.60606061 0.6 0.55172414 0.63333333] mean value: 0.6060193923820176 key: train_jcc value: [0.75918367 0.73809524 0.75903614 0.752 0.72111554 0.71487603 0.75720165 0.75409836 0.71370968 0.76734694] mean value: 0.7436663249990534 MCC on Blind test: -0.1 MCC on Training: 0.48 Running classifier: 12 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(cv=3, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(cv=3, random_state=42))]) key: fit_time value: [0.52367306 0.50741673 0.49810743 0.63048077 0.78237915 0.63401699 0.70979834 0.5006988 0.51225877 0.51098514] mean value: 0.5809815168380738 key: score_time value: [0.0123105 0.01246238 0.01227665 0.01229286 0.01234293 0.01238775 0.01209641 0.01236963 0.0121789 0.01227617] mean value: 0.012299418449401855 key: test_mcc value: [0.5732115 0.75645593 0.74456522 0.74773263 0.87917396 0.60807084 0.79308818 0.55422693 0.73029674 0.87705802] mean value: 0.7263879959244844 key: train_mcc value: [0.99054326 0.90072734 0.93825182 0.92415352 0.9102487 0.9102487 0.91057987 0.94316541 0.95299201 0.9386522 ] mean value: 0.9319562834426032 key: test_fscore value: [0.79310345 0.88 0.86956522 0.875 0.94117647 0.82142857 0.90196078 0.8 0.86792453 0.93877551] mean value: 0.8688934530503667 key: train_fscore value: [0.99528302 0.95104895 0.96926714 0.96244131 0.95550351 0.95550351 0.95571096 0.97169811 0.97663551 0.96955504] mean value: 0.9662647067777896 key: test_precision value: [0.65714286 0.81481481 0.86956522 0.84 0.88888889 0.71875 0.85185185 0.70967742 0.76666667 0.88461538] mean value: 0.8001973100726607 key: train_precision value: [0.99061033 0.93577982 0.96698113 0.95348837 0.94009217 0.94009217 0.93607306 0.96261682 0.96313364 0.95833333] mean value: 0.9547200836794956 key: test_recall value: [1. 0.95652174 0.86956522 0.91304348 1. 0.95833333 0.95833333 0.91666667 1. 1. ] mean value: 0.9572463768115942 key: train_recall value: [1. 0.96682464 0.97156398 0.97156398 0.97142857 0.97142857 0.97619048 0.98095238 0.99052133 0.98104265] mean value: 0.9781516587677725 key: test_accuracy value: [0.74468085 0.87234043 0.87234043 0.87234043 0.93617021 0.78723404 0.89361702 0.76595745 0.84782609 0.93478261] mean value: 0.8527289546716004 key: train_accuracy value: [0.99524941 0.95011876 0.96912114 0.96199525 0.95486936 0.95486936 0.95486936 0.97149644 0.97630332 0.96919431] mean value: 0.9658086703965957 key: test_roc_auc value: [0.75 0.8740942 0.87228261 0.87318841 0.93478261 0.78351449 0.89221014 0.76268116 0.84782609 0.93478261] mean value: 0.852536231884058 key: train_roc_auc value: [0.9952381 0.95007899 0.96911532 0.96197247 0.9549086 0.9549086 0.95491988 0.97151884 0.97630332 0.96919431] mean value: 0.9658158429248477 key: test_jcc value: [0.65714286 0.78571429 0.76923077 0.77777778 0.88888889 0.6969697 0.82142857 0.66666667 0.76666667 0.88461538] mean value: 0.7715101565101564 key: train_jcc value: [0.99061033 0.90666667 0.94036697 0.92760181 0.91479821 0.91479821 0.91517857 0.94495413 0.9543379 0.94090909] mean value: 0.9350221880614443 MCC on Blind test: 0.03 MCC on Training: 0.73 Running classifier: 13 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [2.13620329 1.42204452 1.71286178 1.88426256 2.08965015 1.73018169 2.19596434 1.00858879 1.93313074 2.02586269] mean value: 1.8138750553131104 key: score_time value: [0.01324129 0.01373291 0.01361728 0.01342416 0.013592 0.01256633 0.02379441 0.01299024 0.01217651 0.0124166 ] mean value: 0.014155173301696777 key: test_mcc value: [0.63294907 0.75645593 0.83303222 0.7085716 0.91804649 0.57227835 0.66534784 0.57560058 0.80178373 0.83887049] mean value: 0.7302936305085213 key: train_mcc value: [0.98575942 0.9338663 0.92548525 0.9478412 0.98104223 0.94367934 0.97189305 0.84486476 0.94873074 0.96247452] mean value: 0.9445636800552396 key: test_fscore value: [0.82352941 0.88 0.91666667 0.85714286 0.96 0.80701754 0.84210526 0.8 0.90196078 0.92 ] mean value: 0.8708422526905499 key: train_fscore value: [0.99287411 0.96728972 0.96313364 0.97374702 0.99052133 0.97196262 0.98591549 0.92378753 0.97447796 0.98130841] mean value: 0.9725017821263633 key: test_precision value: [0.75 0.81481481 0.88 0.80769231 0.92307692 0.6969697 0.72727273 0.76923077 0.82142857 0.85185185] mean value: 0.8042337662337662 key: train_precision value: [0.9952381 0.95391705 0.93721973 0.98076923 0.98584906 0.95412844 0.97222222 0.89686099 0.95454545 0.96774194] mean value: 0.9598492203409652 key: test_recall value: [0.91304348 0.95652174 0.95652174 0.91304348 1. 0.95833333 1. 0.83333333 1. 1. ] mean value: 0.9530797101449275 key: train_recall value: [0.99052133 0.98104265 0.99052133 0.96682464 0.9952381 0.99047619 1. 0.95238095 0.99526066 0.99526066] mean value: 0.9857526517716091 key: test_accuracy value: [0.80851064 0.87234043 0.91489362 0.85106383 0.95744681 0.76595745 0.80851064 0.78723404 0.89130435 0.91304348] mean value: 0.8570305272895468 key: train_accuracy value: [0.99287411 0.96674584 0.96199525 0.97387173 0.99049881 0.97149644 0.98574822 0.9216152 0.97393365 0.98104265] mean value: 0.9719821909018247 key: test_roc_auc value: [0.81068841 0.8740942 0.91576087 0.85235507 0.95652174 0.76177536 0.80434783 0.78623188 0.89130435 0.91304348] mean value: 0.8566123188405796 key: train_roc_auc value: [0.99287971 0.9667118 0.96192733 0.97388851 0.99051004 0.97154141 0.98578199 0.92168811 0.97393365 0.98104265] mean value: 0.9719905213270141 key: test_jcc value: [0.7 0.78571429 0.84615385 0.75 0.92307692 0.67647059 0.72727273 0.66666667 0.82142857 0.85185185] mean value: 0.7748635460400167 key: train_jcc value: [0.98584906 0.93665158 0.92888889 0.94883721 0.98122066 0.94545455 0.97222222 0.8583691 0.95022624 0.96330275] mean value: 0.9471022258809073 MCC on Blind test: -0.01 MCC on Training: 0.73 Running classifier: 14 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.01308632 0.01313615 0.00969481 0.00921297 0.00921392 0.00982428 0.00941038 0.00941014 0.00958443 0.00927281] mean value: 0.010184621810913086 key: score_time value: [0.01189017 0.0106442 0.00886798 0.00835896 0.008564 0.00885177 0.00864148 0.00860834 0.00848961 0.00872922] mean value: 0.009164571762084961 key: test_mcc value: [ 0.44874504 0.45455353 0.53483083 0.4078185 -0.14855072 0.31876614 0.06193171 0.40437762 0.21821789 0.22518867] mean value: 0.2925879213975174 key: train_mcc value: [0.44080271 0.3992167 0.45255299 0.45312612 0.23108989 0.27840821 0.25895824 0.31599272 0.45096687 0.445282 ] mean value: 0.3726396441126969 key: test_fscore value: [0.69767442 0.68292683 0.74418605 0.66666667 0.42553191 0.68 0.56 0.72 0.59090909 0.55 ] mean value: 0.6317894966853946 key: train_fscore value: [0.70935961 0.68170426 0.70707071 0.70558376 0.59701493 0.62561576 0.62318841 0.65217391 0.71707317 0.69897959] mean value: 0.6717764100307797 key: test_precision value: [0.75 0.77777778 0.8 0.73684211 0.43478261 0.65384615 0.53846154 0.69230769 0.61904762 0.64705882] mean value: 0.6650124318929003 key: train_precision value: [0.73846154 0.72340426 0.75675676 0.75956284 0.625 0.64795918 0.63235294 0.66176471 0.73869347 0.75690608] mean value: 0.7040861767484542 key: test_recall value: [0.65217391 0.60869565 0.69565217 0.60869565 0.41666667 0.70833333 0.58333333 0.75 0.56521739 0.47826087] mean value: 0.6067028985507247 key: train_recall value: [0.68246445 0.64454976 0.66350711 0.65876777 0.57142857 0.6047619 0.61428571 0.64285714 0.69668246 0.6492891 ] mean value: 0.6428593996840442 key: test_accuracy value: [0.72340426 0.72340426 0.76595745 0.70212766 0.42553191 0.65957447 0.53191489 0.70212766 0.60869565 0.60869565] mean value: 0.6451433857539316 key: train_accuracy value: [0.71971496 0.69833729 0.72446556 0.72446556 0.6152019 0.63895487 0.62945368 0.65795724 0.72511848 0.72037915] mean value: 0.6854048699215365 key: test_roc_auc value: [0.72192029 0.72101449 0.76449275 0.70018116 0.42572464 0.65851449 0.5307971 0.70108696 0.60869565 0.60869565] mean value: 0.6441123188405797 key: train_roc_auc value: [0.71980366 0.69846536 0.7246107 0.72462198 0.61509817 0.63887384 0.62941774 0.65792146 0.72511848 0.72037915] mean value: 0.685431053938163 key: test_jcc value: [0.53571429 0.51851852 0.59259259 0.5 0.27027027 0.51515152 0.38888889 0.5625 0.41935484 0.37931034] mean value: 0.46823012546733345 key: train_jcc value: [0.54961832 0.51711027 0.546875 0.54509804 0.42553191 0.45519713 0.45263158 0.48387097 0.55893536 0.5372549 ] mean value: 0.5072123483362991 MCC on Blind test: -0.02 MCC on Training: 0.29 Running classifier: 15 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.01287723 0.00980163 0.01002502 0.01075315 0.00954342 0.0095365 0.00966239 0.00970721 0.00961494 0.00964499] mean value: 0.010116648674011231 key: score_time value: [0.01203012 0.00870204 0.00858164 0.00884914 0.00875354 0.00868988 0.0085516 0.00862908 0.008847 0.0088203 ] mean value: 0.00904543399810791 key: test_mcc value: [0.54211097 0.38613937 0.3493986 0.25103889 0.19202899 0.29401533 0.33346345 0.23994123 0.28347335 0.24140227] mean value: 0.31130124642099216 key: train_mcc value: [0.42317332 0.42607571 0.47816841 0.49135309 0.41616574 0.39504635 0.40956452 0.44764408 0.39922663 0.50207941] mean value: 0.43884972652928067 key: test_fscore value: [0.73170732 0.59459459 0.4516129 0.5 0.59574468 0.58536585 0.61904762 0.59090909 0.54054054 0.5 ] mean value: 0.5709522599900423 key: train_fscore value: [0.64606742 0.62352941 0.66857143 0.69589041 0.67875648 0.6122449 0.62209302 0.6223565 0.59214502 0.70136986] mean value: 0.6463024438512027 key: test_precision value: [0.83333333 0.78571429 0.875 0.69230769 0.60869565 0.70588235 0.72222222 0.65 0.71428571 0.69230769] mean value: 0.7279748945286031 key: train_precision value: [0.79310345 0.82170543 0.84172662 0.82467532 0.74431818 0.78947368 0.79850746 0.85123967 0.81666667 0.83116883] mean value: 0.8112585313985072 key: test_recall value: [0.65217391 0.47826087 0.30434783 0.39130435 0.58333333 0.5 0.54166667 0.54166667 0.43478261 0.39130435] mean value: 0.4818840579710145 key: train_recall value: [0.5450237 0.50236967 0.55450237 0.60189573 0.62380952 0.5 0.50952381 0.49047619 0.46445498 0.60663507] mean value: 0.5398691040397201 key: test_accuracy value: [0.76595745 0.68085106 0.63829787 0.61702128 0.59574468 0.63829787 0.65957447 0.61702128 0.63043478 0.60869565] mean value: 0.6451896392229417 key: train_accuracy value: [0.70071259 0.695962 0.72446556 0.73634204 0.70546318 0.68408551 0.6912114 0.70308789 0.68009479 0.74170616] mean value: 0.7063131114138083 key: test_roc_auc value: [0.76358696 0.67663043 0.63134058 0.61231884 0.59601449 0.64130435 0.66213768 0.61865942 0.63043478 0.60869565] mean value: 0.6441123188405797 key: train_roc_auc value: [0.70108328 0.69642293 0.72487023 0.73666215 0.70526969 0.68364929 0.69078086 0.70258407 0.68009479 0.74170616] mean value: 0.7063123448431505 key: test_jcc value: [0.57692308 0.42307692 0.29166667 0.33333333 0.42424242 0.4137931 0.44827586 0.41935484 0.37037037 0.33333333] mean value: 0.40343699321730464 key: train_jcc value: [0.47717842 0.45299145 0.50214592 0.53361345 0.51372549 0.44117647 0.45147679 0.45175439 0.42060086 0.54008439] mean value: 0.4784747630905823 MCC on Blind test: -0.06 MCC on Training: 0.31 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.01472759 0.01803899 0.01965237 0.01928186 0.02309656 0.02104068 0.040411 0.02261043 0.0272491 0.02104139] mean value: 0.022714996337890626 key: score_time value: [0.00905967 0.01183963 0.01196671 0.01185036 0.01199174 0.01204753 0.03066134 0.01261353 0.01247525 0.01205039] mean value: 0.013655614852905274 key: test_mcc value: [0.50857834 0.28997216 0.34619073 0.28037519 0.46265195 0.40952208 0.60807084 0.24694731 0.54772256 0.26413527] mean value: 0.39641664237120494 key: train_mcc value: [0.76463893 0.47340233 0.54432697 0.56331596 0.63357857 0.41010818 0.69074602 0.40529781 0.6514379 0.30073124] mean value: 0.5437583896695333 key: test_fscore value: [0.76923077 0.6984127 0.55555556 0.68965517 0.76190476 0.4516129 0.82142857 0.33333333 0.79245283 0.6969697 ] mean value: 0.6570556292663665 key: train_fscore value: [0.88584475 0.76086957 0.68452381 0.79619048 0.82352941 0.46931408 0.85232068 0.47311828 0.8358209 0.70568562] mean value: 0.7287217559904076 key: test_precision value: [0.68965517 0.55 0.76923077 0.57142857 0.61538462 1. 0.71875 0.83333333 0.7 0.53488372] mean value: 0.6982666182721315 key: train_precision value: [0.85462555 0.61583578 0.92 0.6656051 0.7 0.97014925 0.76515152 0.95652174 0.75968992 0.54521964] mean value: 0.7752798492065102 key: test_recall value: [0.86956522 0.95652174 0.43478261 0.86956522 1. 0.29166667 0.95833333 0.20833333 0.91304348 1. ] mean value: 0.7501811594202898 key: train_recall value: [0.91943128 0.99526066 0.5450237 0.99052133 1. 0.30952381 0.96190476 0.31428571 0.92890995 1. ] mean value: 0.7964861205145565 key: test_accuracy value: [0.74468085 0.59574468 0.65957447 0.61702128 0.68085106 0.63829787 0.78723404 0.57446809 0.76086957 0.56521739] mean value: 0.6623959296947272 key: train_accuracy value: [0.88123515 0.68646081 0.74821853 0.74584323 0.78622328 0.65083135 0.83372922 0.65083135 0.81753555 0.58293839] mean value: 0.7383846855264491 key: test_roc_auc value: [0.74728261 0.60326087 0.6548913 0.62228261 0.67391304 0.64583333 0.78351449 0.58242754 0.76086957 0.56521739] mean value: 0.6639492753623188 key: train_roc_auc value: [0.88114421 0.68572557 0.74870232 0.74526066 0.78672986 0.65002257 0.83403295 0.65003385 0.81753555 0.58293839] mean value: 0.7382125930941097 key: test_jcc value: [0.625 0.53658537 0.38461538 0.52631579 0.61538462 0.29166667 0.6969697 0.2 0.65625 0.53488372] mean value: 0.5067671239893939 key: train_jcc value: [0.79508197 0.61403509 0.52036199 0.66139241 0.7 0.30660377 0.74264706 0.30985915 0.71794872 0.54521964] mean value: 0.5913149794475555 MCC on Blind test: -0.11 MCC on Training: 0.4 Running classifier: 17 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.02846289 0.02837276 0.02922511 0.02920532 0.02926826 0.02873731 0.02855325 0.02829218 0.02906728 0.02889252] mean value: 0.028807687759399413 key: score_time value: [0.01016974 0.01281261 0.01286721 0.01282334 0.01278114 0.01263809 0.01252961 0.0127728 0.01244259 0.01288509] mean value: 0.012472224235534669 key: test_mcc value: [1. 1. 0.95833333 0.87917396 0.95825929 1. 1. 0.95825929 1. 1. ] mean value: 0.9754025880672307 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 1. 0.9787234 0.93023256 0.97959184 1. 1. 0.97959184 1. 1. ] mean value: 0.9868139635864243 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 0.95833333 1. 0.96 1. 1. 0.96 1. 1. ] mean value: 0.9878333333333333 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 0.86956522 1. 1. 1. 1. 1. 1. ] mean value: 0.9869565217391305 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 1. 0.9787234 0.93617021 0.9787234 1. 1. 0.9787234 1. 1. ] mean value: 0.9872340425531915 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 1. 0.97916667 0.93478261 0.97826087 1. 1. 0.97826087 1. 1. ] mean value: 0.9870471014492754 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 1. 0.95833333 0.86956522 0.96 1. 1. 0.96 1. 1. ] mean value: 0.9747898550724639 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.0 MCC on Training: 0.98 Running classifier: 18 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42))]) key: fit_time value: [0.6532743 0.65564561 0.65907669 0.64169312 0.68079185 0.66154504 0.65284944 0.60996199 0.68812037 0.66625953] mean value: 0.6569217920303345 key: score_time value: [0.16384745 0.17473102 0.18530393 0.16704321 0.15168524 0.13945627 0.16091037 0.18963671 0.16708207 0.18383861] mean value: 0.16835348606109618 key: test_mcc value: [0.87979456 1. 1. 0.91804649 0.87917396 0.84147165 0.84147165 0.91804649 0.95742711 0.87705802] mean value: 0.9112489931109884 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.93877551 1. 1. 0.95454545 0.94117647 0.92307692 0.92307692 0.96 0.9787234 0.93877551] mean value: 0.9558150195951018 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.88461538 1. 1. 1. 0.88888889 0.85714286 0.85714286 0.92307692 0.95833333 0.88461538] mean value: 0.925381562881563 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 0.91304348 1. 1. 1. 1. 1. 1. ] mean value: 0.9913043478260869 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.93617021 1. 1. 0.95744681 0.93617021 0.91489362 0.91489362 0.95744681 0.97826087 0.93478261] mean value: 0.9530064754856614 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.9375 1. 1. 0.95652174 0.93478261 0.91304348 0.91304348 0.95652174 0.97826087 0.93478261] mean value: 0.9524456521739131 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.88461538 1. 1. 0.91304348 0.88888889 0.85714286 0.85714286 0.92307692 0.95833333 0.88461538] mean value: 0.9166859107076499 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.05 MCC on Training: 0.91 Running classifier: 19 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea...age_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42))]) key: fit_time value: [1.00341678 1.01689363 0.95714116 0.99213171 0.99172735 1.01772118 0.94559336 0.99549794 0.99683237 0.95731091] mean value: 0.9874266386032104 key: score_time value: [0.23025036 0.23570895 0.20647287 0.26085782 0.20399737 0.25643229 0.22633791 0.20224261 0.13757157 0.24527526] mean value: 0.22051470279693602 key: test_mcc value: [0.7023605 0.95825929 0.91485507 0.66243303 0.84147165 0.76896316 0.84147165 0.7876601 0.91304348 0.87705802] mean value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( 0.8267575956942 key: train_mcc value: [0.96675694 0.97625765 0.97153949 0.96216707 0.97634685 0.98575942 0.98104223 0.97149628 0.97160763 0.97173861] mean value: 0.9734712158555151 key: test_fscore value: [0.84444444 0.97777778 0.95652174 0.83333333 0.92307692 0.88888889 0.92307692 0.89795918 0.95652174 0.93877551] mean value: 0.9140376462736711 key: train_fscore value: [0.98337292 0.98817967 0.98584906 0.98122066 0.98817967 0.99287411 0.99052133 0.98571429 0.98584906 0.98591549] mean value: 0.9867676245111119 key: test_precision value: [0.86363636 1. 0.95652174 0.8 0.85714286 0.8 0.85714286 0.88 0.95652174 0.88461538] mean value: 0.8855580940798331 key: train_precision value: [0.98571429 0.98584906 0.98122066 0.97209302 0.98122066 0.99052133 0.98584906 0.98571429 0.98122066 0.97674419] mean value: 0.982614719278365 key: test_recall value: [0.82608696 0.95652174 0.95652174 0.86956522 1. 1. 1. 0.91666667 0.95652174 1. ] mean value: 0.9481884057971015 key: train_recall value: [0.98104265 0.99052133 0.99052133 0.99052133 0.9952381 0.9952381 0.9952381 0.98571429 0.99052133 0.99526066] mean value: 0.9909817197020988 key: test_accuracy value: [0.85106383 0.9787234 0.95744681 0.82978723 0.91489362 0.87234043 0.91489362 0.89361702 0.95652174 0.93478261] mean value: 0.9104070305272897 key: train_accuracy value: [0.98337292 0.98812352 0.98574822 0.98099762 0.98812352 0.99287411 0.99049881 0.98574822 0.98578199 0.98578199] mean value: 0.9867050916909637 key: test_roc_auc value: [0.85054348 0.97826087 0.95742754 0.83061594 0.91304348 0.86956522 0.91304348 0.89311594 0.95652174 0.93478261] mean value: 0.9096920289855073 key: train_roc_auc value: [0.98337847 0.98811781 0.98573685 0.98097495 0.98814037 0.99287971 0.99051004 0.98574814 0.98578199 0.98578199] mean value: 0.98670503272399 key: test_jcc value: [0.73076923 0.95652174 0.91666667 0.71428571 0.85714286 0.8 0.85714286 0.81481481 0.91666667 0.88461538] mean value: 0.8448625931234627 key: train_jcc value: [0.96728972 0.97663551 0.97209302 0.96313364 0.97663551 0.98584906 0.98122066 0.97183099 0.97209302 0.97222222] mean value: 0.9739003356746659 MCC on Blind test: -0.06 MCC on Training: 0.83 Running classifier: 20 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.03532147 0.03663301 0.03705621 0.03713036 0.03878307 0.03837204 0.03616977 0.04034829 0.03840828 0.03824401] mean value: 0.03764665126800537 key: score_time value: [0.01439738 0.02413583 0.02138686 0.02197242 0.0217371 0.02948236 0.02286911 0.02075815 0.02390909 0.0225141 ] mean value: 0.022316241264343263 key: test_mcc value: [0.37275718 0.66243303 0.57560058 0.5326087 0.83243502 0.50052164 0.60807084 0.44874504 0.70164642 0.66226618] mean value: 0.5897084621980706 key: train_mcc value: [0.79184334 0.79011498 0.72525194 0.77922428 0.77371882 0.79902727 0.79189016 0.7837775 0.78872541 0.77830956] mean value: 0.7801883266029235 key: test_fscore value: [0.70588235 0.83333333 0.77272727 0.76595745 0.92 0.77966102 0.82142857 0.74509804 0.85714286 0.84 ] mean value: 0.8041230890546561 key: train_fscore value: [0.89814815 0.89841986 0.86574074 0.8929385 0.88940092 0.90205011 0.89767442 0.89449541 0.89702517 0.89145497] mean value: 0.8927348254017424 key: test_precision value: [0.64285714 0.8 0.80952381 0.75 0.88461538 0.65714286 0.71875 0.7037037 0.80769231 0.77777778] mean value: 0.7552062983312983 key: train_precision value: [0.87782805 0.85775862 0.84615385 0.85964912 0.86160714 0.86462882 0.87727273 0.86283186 0.86725664 0.86936937] mean value: 0.8644356199984321 key: test_recall value: [0.7826087 0.86956522 0.73913043 0.7826087 0.95833333 0.95833333 0.95833333 0.79166667 0.91304348 0.91304348] mean value: 0.8666666666666666 key: train_recall value: [0.91943128 0.94312796 0.88625592 0.92890995 0.91904762 0.94285714 0.91904762 0.92857143 0.92890995 0.91469194] mean value: 0.923085082374182 key: test_accuracy value: [0.68085106 0.82978723 0.78723404 0.76595745 0.91489362 0.72340426 0.78723404 0.72340426 0.84782609 0.82608696] mean value: 0.788667900092507 key: train_accuracy value: [0.89548694 0.89311164 0.86223278 0.88836105 0.88598575 0.89786223 0.89548694 0.89073634 0.89336493 0.88862559] mean value: 0.8891254179284258 key: test_roc_auc value: [0.68297101 0.83061594 0.78623188 0.76630435 0.91394928 0.7182971 0.78351449 0.72192029 0.84782609 0.82608696] mean value: 0.7877717391304347 key: train_roc_auc value: [0.89542993 0.89299255 0.86217558 0.8882645 0.88606409 0.89796886 0.89554277 0.890826 0.89336493 0.88862559] mean value: 0.8891254795757165 key: test_jcc value: [0.54545455 0.71428571 0.62962963 0.62068966 0.85185185 0.63888889 0.6969697 0.59375 0.75 0.72413793] mean value: 0.6765657913287224 key: train_jcc value: [0.81512605 0.81557377 0.76326531 0.80658436 0.80082988 0.82157676 0.81434599 0.80912863 0.81327801 0.80416667] mean value: 0.8063875425410485 MCC on Blind test: -0.02 MCC on Training: 0.59 Running classifier: 21 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=3))]) key: fit_time value: [0.10898447 0.17973995 0.08887506 0.05458832 0.14995623 0.11171579 0.13275743 0.08824372 0.13290691 0.12088013] mean value: 0.11686480045318604 key: score_time value: [0.0248673 0.02048206 0.01238561 0.01242447 0.02875853 0.0275054 0.02874351 0.0140729 0.02897358 0.02878261] mean value: 0.022699594497680664 key: test_mcc value: [0.37275718 0.63294907 0.7876601 0.64834149 0.79308818 0.57227835 0.64404991 0.55422693 0.75056834 0.66226618] mean value: 0.6418185716155288 key: train_mcc value: [0.83199156 0.81361467 0.78056382 0.7917688 0.79461299 0.82252458 0.83588065 0.82696095 0.79822813 0.79822813] mean value: 0.8094374276791351 key: test_fscore value: [0.70588235 0.82352941 0.88888889 0.83018868 0.90196078 0.80701754 0.83636364 0.8 0.88 0.84 ] mean value: 0.8313831297377066 key: train_fscore value: [0.91818182 0.90950226 0.89390519 0.89932886 0.9 0.91324201 0.91954023 0.91533181 0.90160183 0.90160183] mean value: 0.9072235839684277 key: test_precision value: [0.64285714 0.75 0.90909091 0.73333333 0.85185185 0.6969697 0.74193548 0.70967742 0.81481481 0.77777778] mean value: 0.7628308429921333 key: train_precision value: [0.88209607 0.87012987 0.85344828 0.85169492 0.86086957 0.87719298 0.88888889 0.88105727 0.87168142 0.87168142] mean value: 0.8708740668258466 key: test_recall value: [0.7826087 0.91304348 0.86956522 0.95652174 0.95833333 0.95833333 0.95833333 0.91666667 0.95652174 0.91304348] mean value: 0.9182971014492753 key: train_recall value: [0.95734597 0.95260664 0.93838863 0.95260664 0.94285714 0.95238095 0.95238095 0.95238095 0.93364929 0.93364929] mean value: 0.9468246445497629 key: test_accuracy value: [0.68085106 0.80851064 0.89361702 0.80851064 0.89361702 0.76595745 0.80851064 0.76595745 0.86956522 0.82608696] mean value: 0.8121184088806659 key: train_accuracy value: [0.91448931 0.90498812 0.88836105 0.89311164 0.89548694 0.90973872 0.91686461 0.91211401 0.89810427 0.89810427] mean value: 0.9031362925104973 key: test_roc_auc value: [0.68297101 0.81068841 0.89311594 0.8115942 0.89221014 0.76177536 0.80525362 0.76268116 0.86956522 0.82608696] mean value: 0.8115942028985506 key: train_roc_auc value: [0.91438727 0.90487475 0.88824193 0.89296998 0.89559919 0.90983977 0.91694877 0.91220943 0.89810427 0.89810427] mean value: 0.9031279620853079 key: test_jcc value: [0.54545455 0.7 0.8 0.70967742 0.82142857 0.67647059 0.71875 0.66666667 0.78571429 0.72413793] mean value: 0.7148300007888684 key: train_jcc value: [0.8487395 0.8340249 0.80816327 0.81707317 0.81818182 0.84033613 0.85106383 0.84388186 0.82083333 0.82083333] mean value: 0.8303131133731293 MCC on Blind test: -0.1 MCC on Training: 0.64 Running classifier: 22 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.02740622 0.02194476 0.02015543 0.02280784 0.02291942 0.02302861 0.0228889 0.01870251 0.01926231 0.0227437 ] mean value: 0.022185969352722167 key: score_time value: [0.01670432 0.0129528 0.01358414 0.01328158 0.0132246 0.01330805 0.01313567 0.01204491 0.0115006 0.01302314] mean value: 0.013275980949401855 key: test_mcc value: [0.67037015 0.70289855 0.74682354 0.40398551 0.51676308 0.57227835 0.66801039 0.57560058 0.48007936 0.48007936] mean value: 0.5816888856933143 key: train_mcc value: [0.73155318 0.72325683 0.71573889 0.71883066 0.71659393 0.70576819 0.73914524 0.73064863 0.68247212 0.70180811] mean value: 0.7165815781461896 key: test_fscore value: [0.84 0.85106383 0.86363636 0.69565217 0.78571429 0.80701754 0.84615385 0.8 0.72727273 0.72727273] mean value: 0.7943783497609878 key: train_fscore value: [0.87015945 0.86681716 0.86111111 0.86486486 0.86175115 0.85446009 0.87119438 0.86836028 0.84160757 0.85314685] mean value: 0.8613472905691625 key: test_precision value: [0.77777778 0.83333333 0.9047619 0.69565217 0.6875 0.6969697 0.78571429 0.76923077 0.76190476 0.76190476] mean value: 0.7674749465510334 key: train_precision value: [0.8377193 0.82758621 0.84162896 0.82403433 0.83482143 0.84259259 0.85714286 0.84304933 0.83962264 0.83944954] mean value: 0.8387647187637108 key: test_recall value: [0.91304348 0.86956522 0.82608696 0.69565217 0.91666667 0.95833333 0.91666667 0.83333333 0.69565217 0.69565217] mean value: 0.8320652173913043 key: train_recall value: [0.90521327 0.90995261 0.88151659 0.90995261 0.89047619 0.86666667 0.88571429 0.8952381 0.8436019 0.86729858] mean value: 0.8855630783118935 key: test_accuracy value: [0.82978723 0.85106383 0.87234043 0.70212766 0.74468085 0.76595745 0.82978723 0.78723404 0.73913043 0.73913043] mean value: 0.7861239592969472 key: train_accuracy value: [0.86460808 0.85985748 0.85748219 0.85748219 0.85748219 0.85273159 0.86935867 0.86460808 0.84123223 0.8507109 ] mean value: 0.8575553579268499 key: test_roc_auc value: [0.83152174 0.85144928 0.87137681 0.70199275 0.74094203 0.76177536 0.82789855 0.78623188 0.73913043 0.73913043] mean value: 0.7851449275362319 key: train_roc_auc value: [0.8645114 0.85973821 0.85742496 0.85735726 0.85756037 0.85276461 0.86939743 0.86468066 0.84123223 0.8507109 ] mean value: 0.8575378018505979 key: test_jcc value: [0.72413793 0.74074074 0.76 0.53333333 0.64705882 0.67647059 0.73333333 0.66666667 0.57142857 0.57142857] mean value: 0.6624598559730405 key: train_jcc value: [0.77016129 0.76494024 0.75609756 0.76190476 0.75708502 0.74590164 0.77178423 0.76734694 0.72653061 0.74390244] mean value: 0.7565654734243898 MCC on Blind test: -0.1 MCC on Training: 0.58 Running classifier: 23 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.01440859 0.02380633 0.02066255 0.02557087 0.02476478 0.02057624 0.02109766 0.02151465 0.02087927 0.02323437] mean value: 0.021651530265808107 key: score_time value: [0.00854897 0.01138377 0.01182389 0.01189303 0.01192856 0.01185942 0.01183534 0.01194096 0.01258588 0.01200914] mean value: 0.011580896377563477 key: test_mcc value: [0.52364889 0.67037015 0.63762501 0.53176131 0.68369322 0.34619073 0.2168681 0.47920886 0.58549055 0.52223297] mean value: 0.5197089794442371 key: train_mcc value: [0.73588711 0.738249 0.51580148 0.75379111 0.66989731 0.72718239 0.4399453 0.58283066 0.64868417 0.77254655] mean value: 0.6584815091946401 key: test_fscore value: [0.77777778 0.84 0.82142857 0.75555556 0.80952381 0.72413793 0.375 0.77192982 0.80769231 0.76595745] mean value: 0.7449003224382419 key: train_fscore value: [0.87288136 0.87472527 0.77946768 0.87962963 0.81748072 0.86757991 0.48920863 0.8030888 0.8344086 0.88679245] mean value: 0.8105263060528671 key: test_precision value: [0.67741935 0.77777778 0.6969697 0.77272727 0.94444444 0.61764706 0.75 0.66666667 0.72413793 0.75 ] mean value: 0.7377790203282582 key: train_precision value: [0.78927203 0.81557377 0.65079365 0.85972851 0.88826816 0.83333333 1. 0.67532468 0.76377953 0.88262911] mean value: 0.8158702759346991 key: test_recall value: /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:463: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_CV['source_data'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:490: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_BT['source_data'] = 'BT' [0.91304348 0.91304348 1. 0.73913043 0.70833333 0.875 0.25 0.91666667 0.91304348 0.7826087 ] mean value: 0.8010869565217391 key: train_recall value: [0.97630332 0.94312796 0.97156398 0.90047393 0.75714286 0.9047619 0.32380952 0.99047619 0.91943128 0.89099526] mean value: 0.8578086210787633 key: test_accuracy value: [0.74468085 0.82978723 0.78723404 0.76595745 0.82978723 0.65957447 0.57446809 0.72340426 0.7826087 0.76086957] mean value: 0.7458371877890843 key: train_accuracy value: [0.85748219 0.86460808 0.72446556 0.87648456 0.83135392 0.86223278 0.66270784 0.75771971 0.81753555 0.88625592] mean value: 0.8140846101023291 key: test_roc_auc value: [0.74818841 0.83152174 0.79166667 0.76539855 0.83242754 0.6548913 0.58152174 0.7192029 0.7826087 0.76086957] mean value: 0.7468297101449276 key: train_roc_auc value: [0.85719928 0.86442112 0.72387723 0.87642744 0.83117806 0.86233356 0.66190476 0.75827127 0.81753555 0.88625592] mean value: 0.8139404197698037 key: test_jcc value: [0.63636364 0.72413793 0.6969697 0.60714286 0.68 0.56756757 0.23076923 0.62857143 0.67741935 0.62068966] mean value: 0.6069631358430023 key: train_jcc value: [0.77443609 0.77734375 0.63862928 0.78512397 0.69130435 0.76612903 0.32380952 0.67096774 0.71586716 0.79661017] mean value: 0.694022106464908 MCC on Blind test: -0.35 MCC on Training: 0.52 Running classifier: 24 Model_name: XGBoost Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.13153887 0.10307169 0.09927869 0.086905 0.09100986 0.09034801 0.08826566 0.09217095 0.09019494 0.25374746] mean value: 0.11265311241149903 key: score_time value: [0.01178336 0.01258659 0.01090407 0.01078367 0.01091957 0.0109129 0.01128197 0.01157928 0.01356912 0.01166463] mean value: 0.011598515510559081 key: test_mcc value: [0.84254172 1. 0.95833333 0.83243502 0.91804649 0.8047833 0.91804649 1. 0.91651514 0.87705802] mean value: 0.9067759511302496 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.92 1. 0.9787234 0.90909091 0.96 0.90566038 0.96 1. 0.95833333 0.93877551] mean value: 0.9530583534242133 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.85185185 1. 0.95833333 0.95238095 0.92307692 0.82758621 0.92307692 1. 0.92 0.88461538] mean value: 0.9240921575231921 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 0.86956522 1. 1. 1. 1. 1. 1. ] mean value: 0.9869565217391305 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.91489362 1. 0.9787234 0.91489362 0.95744681 0.89361702 0.95744681 1. 0.95652174 0.93478261] mean value: 0.9508325624421833 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.91666667 1. 0.97916667 0.91394928 0.95652174 0.89130435 0.95652174 1. 0.95652174 0.93478261] mean value: 0.9505434782608697 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.85185185 1. 0.95833333 0.83333333 0.92307692 0.82758621 0.92307692 1. 0.92 0.88461538] mean value: 0.9121873956184302 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.12 MCC on Training: 0.91 Extracting tts_split_name: sl Total cols in each df: CV df: 8 metaDF: 17 Adding column: Model_name Total cols in bts df: BT_df: 8 First proceeding to rowbind CV and BT dfs: Final output should have: 25 columns Combinig 2 using pd.concat by row ~ rowbind Checking Dims of df to combine: Dim of CV: (24, 8) Dim of BT: (24, 8) 8 Number of Common columns: 8 These are: ['source_data', 'Precision', 'JCC', 'MCC', 'Recall', 'Accuracy', 'F1', 'ROC_AUC'] Concatenating dfs with different resampling methods [WF]: Split type: sl No. of dfs combining: 2 PASS: 2 dfs successfully combined nrows in combined_df_wf: 48 ncols in combined_df_wf: 8 PASS: proceeding to merge metadata with CV and BT dfs Adding column: Model_name ========================================================= SUCCESS: Ran multiple classifiers ======================================================= Input params: Dim of input df: (858, 175) Data type to split: complete Split type: sl target colname: dst_mode oversampling enabled PASS: x_features has no target variable and no dst column Dropped cols: 2 These were: dst_mode and dst No. of cols in input df: 175 No.of cols dropped: 2 No. of columns for x_features: 173 ------------------------------------------------------------- Successfully generated training and test data: Data used: complete Split type: sl Total no. of input features: 173 --------No. of numerical features: 167 --------No. of categorical features: 6 =========================== Resampling: NONE Baseline =========================== Total data size: 858 Train data size: (792, 173) y_train numbers: Counter({0: 675, 1: 117}) Test data size: (66, 173) y_test_numbers: Counter({0: 56, 1: 10}) y_train ratio: 5.769230769230769 y_test ratio: 5.6 ------------------------------------------------------------- Simple Random OverSampling Counter({0: 675, 1: 675}) (1350, 173) Simple Random UnderSampling Counter({0: 117, 1: 117}) (234, 173) Simple Combined Over and UnderSampling Counter({0: 675, 1: 675}) (1350, 173) SMOTE_NC OverSampling Counter({0: 675, 1: 675}) (1350, 173) Generated Resampled data as below: ================================= Resampling: Random oversampling ================================ Train data size: (1350, 173) y_train numbers: 1350 y_train ratio: 1.0 y_test ratio: 5.6 ================================ Resampling: Random underampling ================================ Train data size: (234, 173) y_train numbers: 234 y_train ratio: 1.0 y_test ratio: 5.6 ================================ Resampling:Combined (over+under) ================================ Train data size: (1350, 173) [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.6s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.4s remaining: 0.7s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.6s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.7s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.7s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.6s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... ñ`B’c>Và;MïYÑ`B’c>Và;MïY[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.6s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.7s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.6s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.3s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Œ loky_p!º¢gUð«Ò ê[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.3s remaining: 0.7s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished y_train numbers: 1350 y_train ratio: 1.0 y_test ratio: 5.6 ============================== Resampling: Smote NC ============================== Train data size: (1350, 173) y_train numbers: 1350 y_train ratio: 1.0 y_test ratio: 5.6 ------------------------------------------------------------- ============================================================== Running several classification models (n): 24 List of models: ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('Gaussian NB', GaussianNB()) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('K-Nearest Neighbors', KNeighborsClassifier()) ('LDA', LinearDiscriminantAnalysis()) ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(cv=3, random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Multinomial', MultinomialNB()) ('Naive Bayes', BernoulliNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=12, random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Random Forest', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42)) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=3)) ('SVC', SVC(random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=12, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ================================================================ Running classifier: 1 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.23182583 0.22612381 0.24022722 0.23586702 0.23265815 0.24828553 0.22825456 0.22576189 0.22587442 0.23138952] mean value: 0.2326267957687378 key: score_time value: [0.01537466 0.0163486 0.01941204 0.01594663 0.01648784 0.01635218 0.01553869 0.0156033 0.01583505 0.01552534] mean value: 0.016242432594299316 key: test_mcc value: [ 0.14619957 0.3363364 0.68315508 0.29875024 0.20102868 0.22397731 0.01179595 0.36445565 -0.06820604 0.38081708] mean value: 0.25783099150331124 key: train_mcc value: [0.63428301 0.61530343 0.6265638 0.60250878 0.59756505 0.67727508 0.56573496 0.65212615 0.63931769 0.60944838] mean value: 0.6220126335745269 key: test_fscore value: [0.22222222 0.44444444 0.72727273 0.35294118 0.3 0.25 0.11111111 0.42105263 0. 0.28571429] mean value: 0.3114758598814326 key: train_fscore value: [0.66285714 0.63030303 0.64285714 0.63218391 0.61445783 0.70718232 0.60115607 0.67058824 0.6627907 0.6440678 ] mean value: 0.6468444174773451 key: test_precision value: [0.33333333 0.4 0.72727273 0.5 0.33333333 0.5 0.16666667 0.57142857 0. 1. ] mean value: 0.4532034632034632 key: train_precision value: [0.82857143 0.86666667 0.87096774 0.80882353 0.85 0.84210526 0.76470588 0.87692308 0.85074627 0.79166667] mean value: 0.835117652434264 key: test_recall value: [0.16666667 0.5 0.72727273 0.27272727 0.27272727 0.16666667 0.08333333 0.33333333 0. 0.16666667] mean value: 0.2689393939393939 key: train_recall value: [0.55238095 0.4952381 0.50943396 0.51886792 0.48113208 0.60952381 0.4952381 0.54285714 0.54285714 0.54285714] mean value: 0.5290386343216531 key: test_accuracy value: [0.825 0.8125 0.92405063 0.86075949 0.82278481 0.84810127 0.79746835 0.86075949 0.82278481 0.87341772] mean value: 0.8447626582278481 key: train_accuracy value: [0.91713483 0.91432584 0.91584853 0.91023843 0.91023843 0.9256662 0.90322581 0.92145863 0.91865358 0.91164095] mean value: 0.9148431221141875 key: test_roc_auc value: [0.55392157 0.68382353 0.84157754 0.61430481 0.59224599 0.56840796 0.50435323 0.64427861 0.48507463 0.58333333] mean value: 0.6071321201479234 key: train_roc_auc value: [0.7663058 0.74102926 0.7481272 0.74872556 0.73315253 0.79489348 0.73446115 0.76484962 0.76320489 0.75909305] mean value: 0.7553842536474302 key: test_jcc value: [0.125 0.28571429 0.57142857 0.21428571 0.17647059 0.14285714 0.05882353 0.26666667 0. 0.16666667] mean value: 0.20079131652661064 key: train_jcc value: [0.4957265 0.46017699 0.47368421 0.46218487 0.44347826 0.54700855 0.42975207 0.50442478 0.49565217 0.475 ] mean value: 0.4787088398020754 MCC on Blind test: 0.45 MCC on Training: 0.26 Running classifier: 2 Model_name: Bagging Classifier Model func: BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3))]) key: fit_time value: [0.45196652 0.47412777 0.44489574 0.47568488 0.44136357 0.45122027 0.4303546 0.49835443 0.40248346 0.47546506] mean value: 0.45459163188934326 key: score_time value: [0.07027864 0.05040026 0.06433845 0.04055452 0.04589272 0.05220604 0.06431103 0.04433322 0.06373358 0.07071948] mean value: 0.05667679309844971 key: test_mcc value: [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 2.7s remaining: 5.3s Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 2.9s remaining: 5.7s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 2.9s remaining: 5.8s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 3.0s remaining: 5.9s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 3.0s remaining: 6.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 3.0s remaining: 1.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 3.0s remaining: 6.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 3.0s remaining: 1.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 3.1s remaining: 1.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 3.1s remaining: 6.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 3.1s remaining: 1.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 3.1s remaining: 1.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 3.1s remaining: 1.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 3.1s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 3.1s remaining: 6.2s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 3.1s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 3.1s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 3.1s remaining: 1.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 3.2s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 3.2s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 3.2s remaining: 6.4s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 3.2s remaining: 6.4s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 3.2s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 3.2s remaining: 1.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 3.2s remaining: 1.1s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 3.2s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 3.3s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 3.3s remaining: 1.1s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 3.3s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 3.3s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.4s remaining: 0.7s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [0.18077538 0.56011203 0.64628973 0.16794369 0.29875024 0.46936541 0.26754663 0.61420864 0.15630552 0.26754663] mean value: 0.3628843887529975 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.23529412 0.6 0.625 0.15384615 0.35294118 0.47058824 0.15384615 0.58823529 0.14285714 0.15384615] mean value: 0.3476454427925016 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.4 0.75 1. 0.5 0.5 0.8 1. 1. 0.5 1. ] mean value: 0.7449999999999999 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.16666667 0.5 0.45454545 0.09090909 0.27272727 0.33333333 0.08333333 0.41666667 0.08333333 0.08333333] mean value: 0.24848484848484848 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8375 0.9 0.92405063 0.86075949 0.86075949 0.88607595 0.86075949 0.91139241 0.84810127 0.86075949] mean value: 0.8750158227848102 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.56127451 0.73529412 0.72727273 0.5381016 0.61430481 0.65920398 0.54166667 0.70833333 0.53420398 0.54166667] mean value: 0.6161322398701679 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.13333333 0.42857143 0.45454545 0.08333333 0.21428571 0.30769231 0.08333333 0.41666667 0.07692308 0.08333333] mean value: 0.22820179820179823 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.36 MCC on Training: 0.36 Running classifier: 3 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.07445025 0.0591557 0.04872394 0.05002379 0.05272031 0.05248356 0.05322647 0.04985237 0.04819942 0.05382323] mean value: 0.05426590442657471 key: score_time value: [0.00923443 0.00959826 0.00950885 0.00942016 0.00974941 0.00963688 0.01028633 0.0098331 0.00989342 0.00979567] mean value: 0.009695649147033691 key: test_mcc value: [ 0.3363364 0.4260261 0.36631016 0.15797627 0.21594923 0.11567164 -0.13195808 0.36921567 0.06488006 0.18125559] mean value: 0.2101663039618646 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.44444444 0.51851852 0.45454545 0.3030303 0.33333333 0.25 0. 0.45454545 0.22222222 0.28571429] mean value: 0.32663540163540167 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.4 0.46666667 0.45454545 0.22727273 0.30769231 0.25 0. 0.5 0.2 0.33333333] mean value: 0.31395104895104897 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 0.58333333 0.45454545 0.45454545 0.36363636 0.25 0. 0.41666667 0.25 0.25 ] mean value: 0.3522727272727273 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8125 0.8375 0.84810127 0.70886076 0.79746835 0.7721519 0.75949367 0.84810127 0.73417722 0.81012658] mean value: 0.7928481012658228 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.68382353 0.73284314 0.68315508 0.60227273 0.61564171 0.55783582 0.44776119 0.6710199 0.53544776 0.58022388] mean value: 0.6110024742597175 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.28571429 0.35 0.29411765 0.17857143 0.2 0.14285714 0. 0.29411765 0.125 0.16666667] mean value: 0.20370448179271708 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.15 MCC on Training: 0.21 Running classifier: 4 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.01181912 0.01176858 0.01305485 0.01297617 0.0113759 0.01200151 0.01267982 0.01273012 0.01269293 0.01197791] mean value: 0.012307691574096679 key: score_time value: [0.01012039 0.00990844 0.00920725 0.00976348 0.00931096 0.0099144 0.00994563 0.00982094 0.00980282 0.00989151] mean value: 0.00976858139038086 key: test_mcc value: [ 0.01960784 0.14002801 0.03352699 0.08594704 0.04946524 0.0917465 0.2074842 0.58134685 -0.0251563 0.09752441] mean value: 0.1281520793679427 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.16666667 0.28571429 0.17391304 0.2 0.18181818 0.2 0.34482759 0.63636364 0.1 0.24 ] mean value: 0.2529303400247928 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.16666667 0.25 0.16666667 0.22222222 0.18181818 0.25 0.29411765 0.7 0.125 0.23076923] mean value: 0.2587260615201792 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.16666667 0.33333333 0.18181818 0.18181818 0.18181818 0.16666667 0.41666667 0.58333333 0.08333333 0.25 ] mean value: 0.2545454545454546 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.75 0.75 0.75949367 0.79746835 0.7721519 0.79746835 0.75949367 0.89873418 0.7721519 0.75949367] mean value: 0.7816455696202531 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.50980392 0.57843137 0.51737968 0.5394385 0.52473262 0.53855721 0.61878109 0.76927861 0.48942786 0.55037313] mean value: 0.5636204006704446 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.09090909 0.16666667 0.0952381 0.11111111 0.1 0.11111111 0.20833333 0.46666667 0.05263158 0.13636364] mean value: 0.15390312903470799 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.13 MCC on Training: 0.13 Running classifier: 5 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.17735219 0.15276575 0.15545011 0.15438962 0.15267587 0.15337276 0.15936542 0.15387702 0.14975691 0.1523571 ] mean value: 0.15613627433776855 key: score_time value: [0.02067709 0.01833534 0.01971769 0.01849818 0.01818538 0.01823664 0.02257395 0.01932216 0.01942182 0.01901507] mean value: 0.01939833164215088 key: test_mcc value: [-0.0829185 0.2248724 0.28152101 0.16794369 -0.06482037 0.03483571 0.32452218 0.46946218 0.26754663 0.15630552] mean value: 0.17792704389497718 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0. 0.25 0.16666667 0.15384615 0. 0.11764706 0.35294118 0.4 0.15384615 0.14285714] mean value: 0.1737804352510235 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0. 0.5 1. 0.5 0. 0.2 0.6 1. 1. 0.5] mean value: 0.53 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0. 0.16666667 0.09090909 0.09090909 0. 0.08333333 0.25 0.25 0.08333333 0.08333333] mean value: 0.10984848484848483 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8125 0.85 0.87341772 0.86075949 0.83544304 0.81012658 0.86075949 0.88607595 0.86075949 0.84810127] mean value: 0.8497943037974685 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.47794118 0.56862745 0.54545455 0.5381016 0.48529412 0.51181592 0.61007463 0.625 0.54166667 0.53420398] mean value: 0.5438180088860511 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0. 0.14285714 0.09090909 0.08333333 0. 0.0625 0.21428571 0.25 0.08333333 0.07692308] mean value: 0.10041416916416916 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.25 MCC on Training: 0.18 Running classifier: 6 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [1.00497222 0.99038506 0.99129963 1.00558424 1.00090003 1.01141405 0.99543476 0.9779706 0.98619175 1.01661611] mean value: 0.9980768442153931 key: score_time value: [0.01025128 0.00977921 0.01038504 0.00936055 0.0100174 0.01044154 0.00917196 0.00938249 0.01023841 0.00991488] mean value: 0.009894275665283203 key: test_mcc value: [0.26782449 0.41201698 0.64628973 0.16073112 0.20102868 0.22397731 0.15630552 0.38483375 0.22397731 0.15630552] mean value: 0.28332904126608127 key: train_mcc value: [0.90291889 0.81885163 0.82656782 0.87449178 0.87449178 0.87342559 0.87342559 0.86745986 0.8911947 0.85546113] mean value: 0.865828876147976 key: test_fscore value: [0.15384615 0.44444444 0.625 0.23529412 0.3 0.25 0.14285714 0.375 0.25 0.14285714] mean value: 0.29192990016519427 key: train_fscore value: [0.9119171 0.82681564 0.83516484 0.88421053 0.88421053 0.88297872 0.88297872 0.87700535 0.90052356 0.86486486] mean value: 0.8750669848176493 key: test_precision value: [1. 0.66666667 1. 0.33333333 0.33333333 0.5 0.5 0.75 0.5 0.5 ] mean value: 0.6083333333333333 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.08333333 0.33333333 0.45454545 0.18181818 0.27272727 0.16666667 0.08333333 0.25 0.16666667 0.08333333] mean value: 0.20757575757575758 key: train_recall value: [0.83809524 0.7047619 0.71698113 0.79245283 0.79245283 0.79047619 0.79047619 0.78095238 0.81904762 0.76190476] mean value: 0.7787601078167116 key: test_accuracy value: [0.8625 0.875 0.92405063 0.83544304 0.82278481 0.84810127 0.84810127 0.87341772 0.84810127 0.84810127] mean value: 0.8585601265822784 key: train_accuracy value: [0.9761236 0.95646067 0.95792426 0.96914446 0.96914446 0.96914446 0.96914446 0.96774194 0.97335203 0.96493689] mean value: 0.9673117228989708 key: test_roc_auc value: [0.54166667 0.65196078 0.72727273 0.56149733 0.59224599 0.56840796 0.53420398 0.61753731 0.56840796 0.53420398] mean value: 0.5897404687790992 key: train_roc_auc value: [0.91904762 0.85238095 0.85849057 0.89622642 0.89622642 0.8952381 0.8952381 0.89047619 0.90952381 0.88095238] mean value: 0.8893800539083558 key: test_jcc value: [0.08333333 0.28571429 0.45454545 0.13333333 0.17647059 0.14285714 0.07692308 0.23076923 0.14285714 0.07692308] mean value: 0.18037266654913714 key: train_jcc value: [0.83809524 0.7047619 0.71698113 0.79245283 0.79245283 0.79047619 0.79047619 0.78095238 0.81904762 0.76190476] mean value: 0.7787601078167116 MCC on Blind test: 0.52 MCC on Training: 0.28 Running classifier: 7 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.01138902 0.01061106 0.01088762 0.01100993 0.01197362 0.01078916 0.01216412 0.01065016 0.01202941 0.01215601] mean value: 0.011366009712219238 key: score_time value: [0.00917602 0.00909925 0.00906396 0.00995493 0.00990629 0.00898886 0.00933194 0.00975227 0.00927615 0.00948429] mean value: 0.009403395652770995 key: test_mcc value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) [0.11988787 0.16886276 0.35526345 0.05233179 0.23705613 0.30416891 0.14118211 0.25723549 0.13046515 0.25695357] mean value: 0.20234072340409842 key: train_mcc value: [0.2955702 0.28710938 0.2797787 0.25825783 0.33554839 0.32711517 0.27514747 0.24447318 0.27968601 0.31841563] mean value: 0.29011019578619773 key: test_fscore value: [0.28571429 0.3255814 0.44444444 0.22857143 0.35897436 0.42424242 0.30769231 0.38888889 0.29411765 0.38709677] mean value: 0.3445323955129348 key: train_fscore value: [0.41071429 0.4047619 0.40277778 0.38575668 0.4437299 0.43589744 0.3963964 0.37426901 0.39772727 0.42996743] mean value: 0.4081998085927966 key: test_precision value: [0.2173913 0.22580645 0.32 0.16666667 0.25 0.33333333 0.22222222 0.29166667 0.22727273 0.31578947] mean value: 0.2570148845806556 key: train_precision value: [0.2987013 0.29437229 0.31868132 0.28138528 0.33658537 0.32850242 0.28947368 0.27004219 0.28340081 0.32673267] mean value: 0.30278773357400685 key: test_recall value: [0.41666667 0.58333333 0.72727273 0.36363636 0.63636364 0.58333333 0.5 0.58333333 0.41666667 0.5 ] mean value: 0.5310606060606061 key: train_recall value: [0.65714286 0.64761905 0.54716981 0.61320755 0.6509434 0.64761905 0.62857143 0.60952381 0.66666667 0.62857143] mean value: 0.6297035040431268 key: test_accuracy value: [0.6875 0.6375 0.74683544 0.65822785 0.6835443 0.75949367 0.65822785 0.72151899 0.69620253 0.75949367] mean value: 0.7008544303797468 key: train_accuracy value: [0.72191011 0.71910112 0.75876578 0.70967742 0.75736325 0.75315568 0.71809257 0.69985975 0.7026648 0.7545582 ] mean value: 0.7295148683360386 key: test_roc_auc value: [0.57598039 0.61519608 0.73863636 0.53475936 0.66377005 0.68718905 0.59328358 0.664801 0.58146766 0.65298507] mean value: 0.630806861414851 key: train_roc_auc value: [0.69512827 0.68954264 0.67144323 0.66986572 0.71344534 0.70950031 0.68106203 0.66249217 0.68777412 0.70244361] mean value: 0.6882697427871924 key: test_jcc value: [0.16666667 0.19444444 0.28571429 0.12903226 0.21875 0.26923077 0.18181818 0.24137931 0.17241379 0.24 ] mean value: 0.209944970938714 key: train_jcc value: [0.25842697 0.25373134 0.25217391 0.23897059 0.28512397 0.27868852 0.24719101 0.23021583 0.24822695 0.27385892] mean value: 0.2566608012477322 MCC on Blind test: 0.35 MCC on Training: 0.2 Running classifier: 8 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.20726848 0.35122085 0.36101818 0.2656064 0.36515355 0.33558846 0.25337458 0.30512595 0.2903657 0.31130719] mean value: 0.3046029329299927 key: score_time value: [0.01869965 0.02392602 0.04725242 0.01816607 0.03064442 0.02057147 0.03113675 0.03816748 0.03464007 0.03037524] mean value: 0.02935795783996582 key: test_mcc value: [ 0.15695699 0.26782449 0.30268562 0.28152101 -0.06482037 -0.06820604 -0.0479188 0.15630552 0. 0.15630552] mean value: 0.11406539252917718 key: train_mcc value: [0.57090198 0.5466878 0.52716179 0.55182113 0.57563046 0.55489915 0.57094865 0.57884128 0.58665071 0.59438033] mean value: 0.565792329234952 key: test_fscore value: [0.14285714 0.15384615 0.28571429 0.16666667 0. 0. 0. 0.14285714 0. 0.14285714] mean value: 0.10347985347985347 key: train_fscore value: [0.53146853 0.5 0.47482014 0.50704225 0.53793103 0.5106383 0.53146853 0.54166667 0.55172414 0.56164384] mean value: 0.5248403432912321 key: test_precision value: [0.5 1. 0.66666667 1. 0. 0. 0. 0.5 0. 0.5 ] mean value: 0.41666666666666663 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.08333333 0.08333333 0.18181818 0.09090909 0. 0. 0. 0.08333333 0. 0.08333333] mean value: 0.06060606060606061 key: train_recall value: [0.36190476 0.33333333 0.31132075 0.33962264 0.36792453 0.34285714 0.36190476 0.37142857 0.38095238 0.39047619] mean value: 0.3561725067385445 key: test_accuracy value: [0.85 0.8625 0.87341772 0.87341772 0.83544304 0.82278481 0.83544304 0.84810127 0.84810127 0.84810127] mean value: 0.8497310126582278 key: train_accuracy value: [0.90589888 0.90168539 0.89761571 0.90182328 0.90603086 0.90322581 0.90603086 0.90743338 0.9088359 0.91023843] mean value: 0.9048818491261799 key: test_roc_auc value: [0.53431373 0.54166667 0.58355615 0.54545455 0.48529412 0.48507463 0.49253731 0.53420398 0.5 0.53420398] mean value: 0.52363051054886 key: train_roc_auc value: [0.68095238 0.66666667 0.65566038 0.66981132 0.68396226 0.67142857 0.68095238 0.68571429 0.69047619 0.6952381 ] mean value: 0.6780862533692723 key: test_jcc value: [0.07692308 0.08333333 0.16666667 0.09090909 0. 0. 0. 0.07692308 0. 0.07692308] mean value: 0.05716783216783217 key: train_jcc value: [0.36190476 0.33333333 0.31132075 0.33962264 0.36792453 0.34285714 0.36190476 0.37142857 0.38095238 0.39047619] mean value: 0.3561725067385445 MCC on Blind test: 0.29 MCC on Training: 0.11 Running classifier: 9 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.01920176 0.01148462 0.01147437 0.01146603 0.01107407 0.01097322 0.01093268 0.01030874 0.01143456 0.01079154] mean value: 0.01191415786743164 key: score_time value: [0.02903986 0.01679993 0.01486135 0.01808476 0.01536632 0.01821613 0.01425815 0.01451898 0.0146625 0.01749587] mean value: 0.017330384254455565 key: test_mcc value: [-0.06726728 0. -0.04554016 0.30268562 -0.04554016 -0.0479188 -0.06820604 0.26754663 -0.0479188 0. ] mean value: 0.024784099949506484 key: train_mcc value: [0.29198236 0.25764616 0.2409172 0.2409172 0.30302202 0.30214839 0.26110935 0.23587951 0.31493177 0.26497712] mean value: 0.27135310787752326 key: test_fscore value: [0. 0. 0. 0.28571429 0. 0. 0. 0.15384615 0. 0. ] mean value: 0.04395604395604396 key: train_fscore value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( [0.2519685 0.19834711 0.18181818 0.18181818 0.26356589 0.25396825 0.21138211 0.19512195 0.26771654 0.224 ] mean value: 0.22297067209262317 key: test_precision value: [0. 0. 0. 0.66666667 0. 0. 0. 1. 0. 0. ] mean value: 0.16666666666666666 key: train_precision value: [0.72727273 0.75 0.73333333 0.73333333 0.73913043 0.76190476 0.72222222 0.66666667 0.77272727 0.7 ] mean value: 0.7306590752242925 key: test_recall value: [0. 0. 0. 0.18181818 0. 0. 0. 0.08333333 0. 0. ] mean value: 0.026515151515151512 key: train_recall value: [0.15238095 0.11428571 0.10377358 0.10377358 0.16037736 0.15238095 0.12380952 0.11428571 0.16190476 0.13333333] mean value: 0.13203054806828393 key: test_accuracy value: [0.825 0.85 0.84810127 0.87341772 0.84810127 0.83544304 0.82278481 0.86075949 0.83544304 0.84810127] mean value: 0.8447151898734176 key: train_accuracy value: [0.86657303 0.86376404 0.86115007 0.86115007 0.86676017 0.86816269 0.86395512 0.86115007 0.86956522 0.86395512] mean value: 0.8646185606000915 key: test_roc_auc value: [0.48529412 0.5 0.49264706 0.58355615 0.49264706 0.49253731 0.48507463 0.54166667 0.49253731 0.5 ] mean value: 0.5065960305424747 key: train_roc_auc value: [0.57124814 0.55384796 0.5485919 0.5485919 0.57524634 0.57207863 0.55779292 0.55220865 0.57684054 0.56173246] mean value: 0.5618179435477118 key: test_jcc value: [0. 0. 0. 0.16666667 0. 0. 0. 0.08333333 0. 0. ] mean value: 0.025 key: train_jcc value: [0.14414414 0.11009174 0.1 0.1 0.15178571 0.14545455 0.11818182 0.10810811 0.15454545 0.12612613] mean value: 0.1258437653965177 MCC on Blind test: -0.05 MCC on Training: 0.02 Running classifier: 10 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.063869 0.06643963 0.08427262 0.06685257 0.04778981 0.0486238 0.09199262 0.06683207 0.07659435 0.05848265] mean value: 0.06717491149902344 key: score_time value: [0.03136706 0.01488662 0.01491523 0.01971459 0.01271749 0.01288128 0.03055191 0.01854634 0.02316427 0.01279879] mean value: 0.01915435791015625 key: test_mcc value: [0.01329087 0.01960784 0.39670668 0.20102868 0.100602 0.32555209 0.21393035 0.24035222 0.0702541 0.22397731] mean value: 0.18053021463990296 key: train_mcc value: [0.52785291 0.46627817 0.44508197 0.42326093 0.48482939 0.47673466 0.46495747 0.4681297 0.45299083 0.45658463] mean value: 0.4666700652872671 key: test_fscore value: [0.11111111 0.16666667 0.47619048 0.3 0.24 0.4 0.33333333 0.31578947 0.19047619 0.25 ] mean value: 0.2783567251461988 key: train_fscore value: [0.57142857 0.51724138 0.5 0.48 0.53409091 0.53333333 0.51977401 0.51461988 0.50574713 0.51136364] mean value: 0.5187598850303947 key: test_precision value: [0.16666667 0.16666667 0.5 0.33333333 0.21428571 0.5 0.33333333 0.42857143 0.22222222 0.5 ] mean value: 0.33650793650793653 key: train_precision value: [0.71428571 0.65217391 0.62857143 0.60869565 0.67142857 0.64 0.63888889 0.66666667 0.63768116 0.63380282] mean value: 0.649219481138036 key: test_recall value: [0.08333333 0.16666667 0.45454545 0.27272727 0.27272727 0.33333333 0.33333333 0.25 0.16666667 0.16666667] mean value: 0.24999999999999994 key: train_recall value: [0.47619048 0.42857143 0.41509434 0.39622642 0.44339623 0.45714286 0.43809524 0.41904762 0.41904762 0.42857143] mean value: 0.43213836477987416 key: test_accuracy value: [0.8 0.75 0.86075949 0.82278481 0.75949367 0.84810127 0.79746835 0.83544304 0.78481013 0.84810127] mean value: 0.8106962025316455 key: train_accuracy value: [0.89466292 0.88202247 0.87657784 0.87237027 0.88499299 0.88218794 0.88078541 0.88359046 0.87938289 0.87938289] mean value: 0.8815956080495454 key: test_roc_auc value: [0.50490196 0.50980392 0.69050802 0.59224599 0.55548128 0.63681592 0.60696517 0.59514925 0.53109453 0.56840796] mean value: 0.5791374012291485 key: train_roc_auc value: [0.72162077 0.69451636 0.68613037 0.67587268 0.70275248 0.70636748 0.69766604 0.6914317 0.6889646 0.69290414] mean value: 0.6958226615367332 key: test_jcc value: [0.05882353 0.09090909 0.3125 0.17647059 0.13636364 0.25 0.2 0.1875 0.10526316 0.14285714] mean value: 0.16606871456716657 key: train_jcc value: [0.4 0.34883721 0.33333333 0.31578947 0.36434109 0.36363636 0.35114504 0.34645669 0.33846154 0.34351145] mean value: 0.35055121851520943 MCC on Blind test: 0.49 MCC on Training: 0.18 Running classifier: 11 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.07380056 0.04258347 0.04204607 0.04167199 0.04149532 0.0419476 0.04317498 0.04117465 0.04396319 0.04357147] mean value: 0.04554293155670166 key: score_time value: [0.01226473 0.01210165 0.01237965 0.01236963 0.01251268 0.01248717 0.01237941 0.01240087 0.01252794 0.01241183] mean value: 0.012383556365966797 key: test_mcc value: [ 0.06424926 0.32539569 0.16794369 0.16794369 -0.10454687 0.15630552 -0.0479188 0.46946218 0.06312088 0.26754663] mean value: 0.15295018594513407 key: train_mcc value: [0.34950959 0.2219403 0.25364589 0.30927886 0.38748467 0.30718262 0.3383659 0.25160895 0.30859143 0.32032833] mean value: 0.3047936540123323 key: test_fscore value: [0.125 0.35294118 0.15384615 0.15384615 0. 0.14285714 0. 0.4 0.125 0.15384615] mean value: 0.16073367808661926 key: train_fscore value: [0.33576642 0.21538462 0.22047244 0.30434783 0.37142857 0.2962963 0.32352941 0.23255814 0.31428571 0.29007634] mean value: 0.2904145774962152 key: test_precision value: [0.25 0.6 0.5 0.5 0. 0.5 0. 1. 0.25 1. ] mean value: 0.45999999999999996 key: train_precision value: [0.71875 0.56 0.66666667 0.65625 0.76470588 0.66666667 0.70967742 0.625 0.62857143 0.73076923] mean value: 0.6727057294381772 key: test_recall value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( [0.08333333 0.25 0.09090909 0.09090909 0. 0.08333333 0. 0.25 0.08333333 0.08333333] mean value: 0.10151515151515152 key: train_recall value: [0.21904762 0.13333333 0.13207547 0.19811321 0.24528302 0.19047619 0.20952381 0.14285714 0.20952381 0.18095238] mean value: 0.1861185983827493 key: test_accuracy value: [0.825 0.8625 0.86075949 0.86075949 0.79746835 0.84810127 0.83544304 0.88607595 0.82278481 0.86075949] mean value: 0.8459651898734177 key: train_accuracy value: [0.87219101 0.85674157 0.86115007 0.86535764 0.87657784 0.86676017 0.87096774 0.86115007 0.86535764 0.86956522] mean value: 0.8665818979781583 key: test_roc_auc value: [0.51960784 0.61029412 0.5381016 0.5381016 0.46323529 0.53420398 0.49253731 0.625 0.51927861 0.54166667] mean value: 0.538202703062229 key: train_roc_auc value: [0.6021103 0.55760571 0.56027167 0.58999565 0.61605172 0.58701441 0.59736059 0.56402726 0.59407112 0.58471961] mean value: 0.5853228039169447 key: test_jcc value: [0.06666667 0.21428571 0.08333333 0.08333333 0. 0.07692308 0. 0.25 0.06666667 0.08333333] mean value: 0.09245421245421245 key: train_jcc value: [0.20175439 0.12068966 0.12389381 0.17948718 0.22807018 0.17391304 0.19298246 0.13157895 0.18644068 0.16964286] mean value: 0.17084531834688282 MCC on Blind test: 0.0 MCC on Training: 0.15 Running classifier: 12 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(cv=3, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(cv=3, random_state=42))]) key: fit_time value: [0.49715042 0.70521092 0.56786084 0.55812669 0.52510738 0.60777044 0.55432105 0.59228611 0.50186205 0.63403606] mean value: 0.574373197555542 key: score_time value: [0.01247025 0.01292634 0.0124774 0.01254201 0.01248121 0.02212572 0.01241517 0.01249814 0.01239681 0.01284051] mean value: 0.013517355918884278 key: test_mcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_mcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_fscore value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_fscore value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_precision value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_precision value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_recall value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_recall value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_accuracy value: [0.85 0.85 0.86075949 0.86075949 0.86075949 0.84810127 0.84810127 0.84810127 0.84810127 0.84810127] mean value: 0.8522784810126582 key: train_accuracy value: [0.85252809 0.85252809 0.8513324 0.8513324 0.8513324 0.85273492 0.85273492 0.85273492 0.85273492 0.85273492] mean value: 0.8522727989031942 key: test_roc_auc value: [0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5] mean value: 0.5 key: train_roc_auc value: [0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5] mean value: 0.5 key: test_jcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_jcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 MCC on Blind test: 0.0 MCC on Training: 0.0 Running classifier: 13 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [2.94087243 3.16932392 2.83774137 1.93189573 2.98095369 3.23842382 2.66213632 2.95385718 2.32431746 3.06657243] mean value: 2.8106094360351563 key: score_time value: [0.01258135 0.0124867 0.0124321 0.01241159 0.01283431 0.01240301 0.01240921 0.01254892 0.01306963 0.01244569] mean value: 0.012562251091003418 key: test_mcc value: [ 0.11769501 0.48936348 0.43119194 0.19578777 -0.04315055 0.24035222 0.18125559 0.24035222 0.11624879 0.22397731] mean value: 0.219307378601968 key: train_mcc value: [0.7678111 0.84871413 0.82573401 0.57241128 0.80761816 0.88529249 0.81783703 0.87285891 0.78824921 0.84264845] mean value: 0.8029174778695923 key: test_fscore value: [0.21052632 0.52631579 0.5 0.25 0.0952381 0.31578947 0.28571429 0.31578947 0.21052632 0.25 ] mean value: 0.2959899749373434 key: train_fscore value: [0.77456647 0.86170213 0.83870968 0.57324841 0.81767956 0.89473684 0.8342246 0.88659794 0.8125 0.85561497] mean value: 0.8149580597163837 key: test_precision value: [0.28571429 0.71428571 0.55555556 0.4 0.1 0.42857143 0.33333333 0.42857143 0.28571429 0.5 ] mean value: 0.4031746031746032 key: train_precision value: [0.98529412 0.97590361 0.975 0.88235294 0.98666667 1. 0.95121951 0.96629213 0.89655172 0.97560976] mean value: 0.9594890467210103 key: test_recall value: [0.16666667 0.41666667 0.45454545 0.18181818 0.09090909 0.25 0.25 0.25 0.16666667 0.16666667] mean value: 0.23939393939393935 key: train_recall value: [0.63809524 0.77142857 0.73584906 0.4245283 0.69811321 0.80952381 0.74285714 0.81904762 0.74285714 0.76190476] mean value: 0.7144204851752021 key: test_accuracy value: [0.8125 0.8875 0.87341772 0.84810127 0.75949367 0.83544304 0.81012658 0.83544304 0.81012658 0.84810127] mean value: 0.8320253164556961 key: train_accuracy value: [0.94522472 0.96348315 0.95792426 0.90603086 0.95371669 0.97194951 0.95652174 0.96914446 0.94950912 0.96213184] mean value: 0.9535636336416786 key: test_roc_auc value: [0.54656863 0.69362745 0.69786096 0.56885027 0.47927807 0.59514925 0.58022388 0.59514925 0.5460199 0.56840796] mean value: 0.5871135632000426 key: train_roc_auc value: [0.8182239 0.88406684 0.86627708 0.70732181 0.84823288 0.9047619 0.8681391 0.9070567 0.86402726 0.87930764] mean value: 0.8547415115549123 key: test_jcc value: [0.11764706 0.35714286 0.33333333 0.14285714 0.05 0.1875 0.16666667 0.1875 0.11764706 0.14285714] mean value: 0.18031512605042016 key: train_jcc value: [0.63207547 0.75700935 0.72222222 0.40178571 0.69158879 0.80952381 0.71559633 0.7962963 0.68421053 0.74766355] mean value: 0.6957972052860165 MCC on Blind test: 0.17 MCC on Training: 0.22 Running classifier: 14 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.01458669 0.014292 0.01072979 0.01048708 0.01022458 0.01018786 0.01023769 0.01138806 0.01146197 0.01062417] mean value: 0.011421990394592286 key: score_time value: [0.01196241 0.00952148 0.00930452 0.0089736 0.00887561 0.00867319 0.0087986 0.00943017 0.00905919 0.009619 ] mean value: 0.009421777725219727 key: test_mcc value: [-0.04726315 -0.06726728 0.24065419 0.28152101 -0.079909 0. 0. 0.26754663 -0.0479188 0.26754663] mean value: 0.0814910221762829 key: train_mcc value: [0.14080596 0.13017462 0.11778305 0.07609331 0.21894371 0.17276957 0.13212898 0.09481928 0.14065677 0.18002073] mean value: 0.1404195990053793 key: test_fscore value: [0. 0. 0.26666667 0.16666667 0. 0. 0. 0.15384615 0. 0.15384615] mean value: 0.0741025641025641 key: train_fscore value: [0.11764706 0.1025641 0.0862069 0.08264463 0.17886179 0.12068966 0.11666667 0.07017544 0.10344828 0.13559322] mean value: 0.11144977312930396 key: test_precision value: [0. 0. 0.5 1. 0. 0. 0. 1. 0. 1. ] mean value: 0.35 key: train_precision value: [0.5 0.5 0.5 0.33333333 0.64705882 0.63636364 0.46666667 0.44444444 0.54545455 0.61538462] mean value: 0.5188706065176654 key: test_recall value: [0. 0. 0.18181818 0.09090909 0. 0. 0. 0.08333333 0. 0.08333333] mean value: 0.04393939393939393 key: train_recall value: [0.06666667 0.05714286 0.04716981 0.04716981 0.10377358 0.06666667 0.06666667 0.03809524 0.05714286 0.07619048] mean value: 0.06266846361185985 key: test_accuracy value: [0.8375 0.825 0.86075949 0.87341772 0.82278481 0.84810127 0.84810127 0.86075949 0.83544304 0.86075949] mean value: 0.8472626582278482 key: train_accuracy value: [0.85252809 0.85252809 0.8513324 0.84431978 0.85834502 0.8569425 0.8513324 0.8513324 0.85413745 0.8569425 ] mean value: 0.8529740611752838 key: test_roc_auc value: [0.49264706 0.48529412 0.57620321 0.54545455 0.47794118 0.5 0.5 0.54166667 0.49253731 0.54166667] mean value: 0.5153410753718041 key: train_roc_auc value: [0.52756727 0.52362909 0.51946629 0.51534767 0.54694445 0.53004386 0.52675439 0.51493578 0.52445959 0.5339834 ] mean value: 0.5263131780905047 key: test_jcc value: [0. 0. 0.15384615 0.09090909 0. 0. 0. 0.08333333 0. 0.08333333] mean value: 0.04114219114219114 key: train_jcc value: [0.0625 0.05405405 0.04504505 0.04310345 0.09821429 0.06422018 0.0619469 0.03636364 0.05454545 0.07272727] mean value: 0.05927202828667163 MCC on Blind test: 0.0 MCC on Training: 0.08 Running classifier: 15 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.01542306 0.01280332 0.01068616 0.01055837 0.01051307 0.0107882 0.01058364 0.01074457 0.01055312 0.01060987] mean value: 0.011326336860656738 key: score_time value: [0.01284719 0.00937366 0.00907731 0.00895476 0.00915337 0.00878143 0.00872183 0.00871873 0.00881171 0.00881767] mean value: 0.009325766563415527 key: test_mcc value: [ 0.01329087 0.18077538 0.02271201 -0.06482037 -0.05614973 0.28494719 -0.08408278 0.10043221 0.06312088 0.01179595] mean value: 0.04720216056078818 key: train_mcc value: [0.18320543 0.18320543 0.12486218 0.17371305 0.18208051 0.14372329 0.18036066 0.15842328 0.16498391 0.15255644] mean value: 0.16471141753926688 key: test_fscore value: [0.11111111 0.23529412 0.11764706 0. 0.09090909 0.26666667 0. 0.13333333 0.125 0.11111111] mean value: 0.11910724896019013 key: train_fscore value: [0.22377622 0.22377622 0.17931034 0.21126761 0.25766871 0.18571429 0.21428571 0.18978102 0.21768707 0.21333333] mean value: 0.2116600539731354 key: test_precision value: [0.16666667 0.4 0.16666667 0. 0.09090909 0.66666667 0. 0.33333333 0.25 0.16666667] mean value: 0.22409090909090906 key: train_precision value: [0.42105263 0.42105263 0.33333333 0.41666667 0.36842105 0.37142857 0.42857143 0.40625 0.38095238 0.35555556] mean value: 0.39032842522974104 key: test_recall value: [0.08333333 0.16666667 0.09090909 0. 0.09090909 0.16666667 0. 0.08333333 0.08333333 0.08333333] mean value: 0.08484848484848487 key: train_recall value: [0.15238095 0.15238095 0.12264151 0.14150943 0.19811321 0.12380952 0.14285714 0.12380952 0.15238095 0.15238095] mean value: 0.14622641509433962 key: test_accuracy value: [0.8 0.8375 0.81012658 0.83544304 0.74683544 0.86075949 0.81012658 0.83544304 0.82278481 0.79746835] mean value: 0.8156487341772152 key: train_accuracy value: [0.84410112 0.84410112 0.83309958 0.84291725 0.83029453 0.8401122 0.8457223 0.84431978 0.83870968 0.8345021 ] mean value: 0.8397879666545851 key: test_roc_auc value: [0.50490196 0.56127451 0.50868984 0.48529412 0.47192513 0.57587065 0.47761194 0.52674129 0.51927861 0.50435323] mean value: 0.5135941282890361 key: train_roc_auc value: [0.55806857 0.55806857 0.53990395 0.55345653 0.56940257 0.54381266 0.5549812 0.54627976 0.5548089 0.55234179] mean value: 0.5531124488547221 key: test_jcc value: [0.05882353 0.13333333 0.0625 0. 0.04761905 0.15384615 0. 0.07142857 0.06666667 0.05882353] mean value: 0.06530408317173023 key: train_jcc value: [0.12598425 0.12598425 0.09848485 0.11811024 0.14788732 0.1023622 0.12 0.10483871 0.1221374 0.11940299] mean value: 0.1185192216642599 MCC on Blind test: 0.17 MCC on Training: 0.05 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=12, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.02591681 0.02370048 0.01759934 0.02114773 0.0219543 0.01830721 0.01755285 0.01966429 0.02041459 0.02215624] mean value: 0.020841383934020997 key: score_time value: [0.01222658 0.01257634 0.01206827 0.01216078 0.01211476 0.01218772 0.01213551 0.01245093 0.01227903 0.01276255] mean value: 0.012296247482299804 key: test_mcc value: [ 0.18295348 0. 0.16794369 0.13563771 0.1488322 -0.0479188 0.13539745 0. 0.08408278 0. ] mean value: 0.08069285039034707 key: train_mcc value: [ 0.30454798 0.09017059 0.22500439 0.26732315 0.37979419 0.1191854 0.26069129 -0.0155741 0.06494754 0.05279244] mean value: 0.17488828750161436 key: test_fscore value: [0.33333333 0. 0.15384615 0.28571429 0.3 0. 0.26086957 0. 0.27272727 0. ] mean value: 0.1606490610838437 key: train_fscore value: [0.39732143 0.01886792 0.16666667 0.35934664 0.47384615 0.07142857 0.37719298 0. 0.26217228 0.01869159] mean value: 0.21455342433947439 key: test_precision value: [0.22222222 0. 0.5 0.17307692 0.20689655 0. 0.27272727 0. 0.15789474 0. ] mean value: 0.15328177065926613 key: train_precision value: [0.25947522 1. 0.71428571 0.22247191 0.35159817 0.57142857 0.3495935 0. 0.15086207 0.5 ] mean value: 0.4119715152901996 key: test_recall value: [0.66666667 0. 0.09090909 0.81818182 0.54545455 0. 0.25 0. 1. 0. ] mean value: 0.3371212121212121 key: train_recall value: [0.84761905 0.00952381 0.09433962 0.93396226 0.72641509 0.03809524 0.40952381 0. 1. 0.00952381] mean value: 0.40690026954177894 key: test_accuracy value: [0.6 0.85 0.86075949 0.43037975 0.64556962 0.83544304 0.78481013 0.84810127 0.18987342 0.84810127] mean value: 0.6893037974683545 key: train_accuracy value: [0.62078652 0.85393258 0.85974755 0.50490884 0.7601683 0.85413745 0.80084151 0.8513324 0.17110799 0.85273492] mean value: 0.7129698063255433 key: test_roc_auc value: [0.62745098 0.5 0.5381016 0.59291444 0.60360963 0.49253731 0.56529851 0.5 0.52238806 0.5 ] mean value: 0.544230052943837 key: train_roc_auc value: [0.71458382 0.5047619 0.54387492 0.68197289 0.74623885 0.51658051 0.63897243 0.49917763 0.51398026 0.50393954] mean value: 0.5864082766247003 key: test_jcc value: [0.2 0. 0.08333333 0.16666667 0.17647059 0. 0.15 0. 0.15789474 0. ] mean value: 0.09343653250773994 key: train_jcc value: [0.24791086 0.00952381 0.09090909 0.21902655 0.31048387 0.03703704 0.23243243 0. 0.15086207 0.00943396] mean value: 0.13076196842820959 MCC on Blind test: 0.2 MCC on Training: 0.08 Running classifier: 17 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.03254032 0.03367853 0.03234601 0.0335784 0.03353095 0.03253102 0.03997612 0.03206062 0.03300571 0.05200624] mean value: 0.035525393486022946 key: score_time value: [0.01292467 0.01275706 0.01346397 0.01273632 0.01287341 0.01275158 0.01284456 0.01288605 0.0128355 0.0128274 ] mean value: 0.012890052795410157 key: test_mcc value: [-0.06726728 0.15695699 0.16794369 -0.06482037 -0.079909 -0.0479188 -0.0479188 -0.06820604 -0.08408278 0.15630552] mean value: 0.002108311900833079 key: train_mcc value: [0.12761024 0.15640016 0.17974147 0.12691753 0.15555102 0.12762547 0.12762547 0.12762547 0.12762547 0.09018144] mean value: 0.13469037196668343 key: test_fscore value: [0. 0.14285714 0.15384615 0. 0. 0. 0. 0. 0. 0.14285714] mean value: 0.04395604395604395 key: train_fscore value: [0.03738318 0.05555556 0.07272727 0.03703704 0.05504587 0.03738318 0.03738318 0.03738318 0.03738318 0.01886792] mean value: 0.04261495492582675 key: test_precision value: [0. 0.5 0.5 0. 0. 0. 0. 0. 0. 0.5] mean value: 0.15 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0. 0.08333333 0.09090909 0. 0. 0. 0. 0. 0. 0.08333333] mean value: 0.025757575757575757 key: train_recall value: [0.01904762 0.02857143 0.03773585 0.01886792 0.02830189 0.01904762 0.01904762 0.01904762 0.01904762 0.00952381] mean value: 0.02182389937106918 key: test_accuracy value: [0.825 0.85 0.86075949 0.83544304 0.82278481 0.83544304 0.83544304 0.82278481 0.81012658 0.84810127] mean value: 0.8345886075949366 key: train_accuracy value: [0.85533708 0.85674157 0.8569425 0.85413745 0.85553997 0.85553997 0.85553997 0.85553997 0.85553997 0.85413745] mean value: 0.8554995902737288 key: test_roc_auc value: [0.48529412 0.53431373 0.5381016 0.48529412 0.47794118 0.49253731 0.49253731 0.48507463 0.47761194 0.53420398] mean value: 0.500290991566233 key: train_roc_auc value: [0.50952381 0.51428571 0.51886792 0.50943396 0.51415094 0.50952381 0.50952381 0.50952381 0.50952381 0.5047619 ] mean value: 0.5109119496855345 key: test_jcc value: [0. 0.07692308 0.08333333 0. 0. 0. 0. 0. 0. 0.07692308] mean value: 0.023717948717948717 key: train_jcc value: [0.01904762 0.02857143 0.03773585 0.01886792 0.02830189 0.01904762 0.01904762 0.01904762 0.01904762 0.00952381] mean value: 0.02182389937106918 MCC on Blind test: 0.0 MCC on Training: 0.0 Running classifier: 18 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42))]) key: fit_time value: [0.77390957 0.74420714 0.74406338 0.74907303 0.77295923 0.78951812 0.79088092 0.83758259 0.78157449 0.79345512] mean value: 0.7777223587036133 key: score_time value: [0.18054175 0.16436887 0.16277003 0.16687989 0.14343691 0.17844343 0.15671396 0.17608118 0.17960715 0.15391588] mean value: 0.16627590656280516 key: test_mcc value: [ 0.10134483 0.46987149 0.28152101 0. -0.04554016 0.28494719 0.10043221 0.46946218 0.26754663 0.26754663] mean value: 0.21971320013008028 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.13333333 0.4 0.16666667 0. 0. 0.26666667 0.13333333 0.4 0.15384615 0.15384615] mean value: 0.18076923076923074 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.33333333 1. 1. 0. 0. 0.66666667 0.33333333 1. 1. 1. ] mean value: 0.6333333333333333 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.08333333 0.25 0.09090909 0. 0. 0.16666667 0.08333333 0.25 0.08333333 0.08333333] mean value: 0.10909090909090909 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8375 0.8875 0.87341772 0.86075949 0.84810127 0.86075949 0.83544304 0.88607595 0.86075949 0.86075949] mean value: 0.8611075949367087 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.52696078 0.625 0.54545455 0.5 0.49264706 0.57587065 0.52674129 0.625 0.54166667 0.54166667] mean value: 0.5501007662223641 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.07142857 0.25 0.09090909 0. 0. 0.15384615 0.07142857 0.25 0.08333333 0.08333333] mean value: 0.10542790542790544 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.31 MCC on Training: 0.22 Running classifier: 19 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea...age_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42))]) key: fit_time value: [1.10370421 1.09422708 1.10288358 1.17262793 1.13070178 1.08145142 1.06592989 1.10370398 1.09320092 1.12563944] mean value: 1.107407021522522 key: score_time value: [0.26303887 0.27092576 0.27034998 0.14823771 0.24847984 0.13043165 0.23733425 0.22376895 0.25941968 0.26377988] mean value: 0.23157665729522706 key: test_mcc value: [ 0.10134483 0.26782449 0.28152101 0. -0.04554016 0.15630552 0. 0.38081708 0.26754663 0. ] mean value: 0.1409819386261199 key: train_mcc value: [0.56292258 0.53842387 0.51873177 0.53548337 0.55182113 0.54673429 0.57884128 0.53010226 0.54673429 0.56296922] mean value: 0.5472764071759983 key: test_fscore value: [0.13333333 0.15384615 0.16666667 0. 0. 0.14285714 0. 0.28571429 0.15384615 0. ] mean value: 0.10362637362637361 key: train_fscore value: [0.52112676 0.48920863 0.46376812 0.48571429 0.50704225 0.5 0.54166667 0.47826087 0.5 0.52112676] mean value: 0.5007914345629612 key: test_precision value: [0.33333333 1. 1. 0. 0. 0.5 0. 1. 1. 0. ] mean value: 0.4833333333333333 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.08333333 0.08333333 0.09090909 0. 0. 0.08333333 0. 0.16666667 0.08333333 0. ] mean value: 0.0590909090909091 key: train_recall value: [0.35238095 0.32380952 0.30188679 0.32075472 0.33962264 0.33333333 0.37142857 0.31428571 0.33333333 0.35238095] mean value: 0.33432165318957774 key: test_accuracy value: [0.8375 0.8625 0.87341772 0.86075949 0.84810127 0.84810127 0.84810127 0.87341772 0.86075949 0.84810127] mean value: 0.8560759493670886 key: train_accuracy value: [0.90449438 0.9002809 0.89621318 0.89901823 0.90182328 0.90182328 0.90743338 0.89901823 0.90182328 0.90462833] mean value: 0.9016556487069984 key: test_roc_auc value: [0.52696078 0.54166667 0.54545455 0.5 0.49264706 0.53420398 0.5 0.58333333 0.54166667 0.5 ] mean value: 0.526593303535797 key: train_roc_auc value: [0.67619048 0.66190476 0.6509434 0.66037736 0.66981132 0.66666667 0.68571429 0.65714286 0.66666667 0.67619048] mean value: 0.6671608265947889 key: test_jcc value: [0.07142857 0.08333333 0.09090909 0. 0. 0.07692308 0. 0.16666667 0.08333333 0. ] mean value: 0.05725940725940726 key: train_jcc value: [0.35238095 0.32380952 0.30188679 0.32075472 0.33962264 0.33333333 0.37142857 0.31428571 0.33333333 0.35238095] mean value: 0.33432165318957774 MCC on Blind test: 0.0 MCC on Training: 0.14 Running classifier: 20 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.03653502 0.04121995 0.04666567 0.04258227 0.0462873 0.04040337 0.04020357 0.04068995 0.04094028 0.01668215] mean value: 0.0392209529876709 key: score_time value: [0.01896834 0.01916409 0.01933336 0.01932597 0.01918745 0.01910114 0.01892948 0.01882625 0.01353836 0.01219726] mean value: 0.017857170104980467 key: test_mcc value: [-0.0829185 0.32539569 0.11138831 -0.04554016 -0.11530711 0.10043221 -0.06820604 0.46946218 -0.06820604 -0.0479188 ] mean value: 0.057858172483302425 key: train_mcc value: [0.40569141 0.32026214 0.323598 0.36640238 0.38370301 0.3268965 0.36869808 0.29921396 0.36669577 0.33225064] mean value: 0.349341189919964 key: test_fscore value: [0. 0.35294118 0.14285714 0. 0. 0.13333333 0. 0.4 0. 0. ] mean value: 0.10291316526610643 key: train_fscore value: [0.37037037 0.29007634 0.26771654 0.34782609 0.35294118 0.31111111 0.35036496 0.27480916 0.36619718 0.3030303 ] mean value: 0.32344432261574124 key: test_precision value: [0. 0.6 0.33333333 0. 0. 0.33333333 0. 1. 0. 0. ] mean value: 0.22666666666666666 key: train_precision value: [0.83333333 0.73076923 0.80952381 0.75 0.8 0.7 0.75 0.69230769 0.7027027 0.74074074] mean value: 0.7509377509377508 key: test_recall value: [0. 0.25 0.09090909 0. 0. 0.08333333 0. 0.25 0. 0. ] mean value: 0.06742424242424243 key: train_recall value: [0.23809524 0.18095238 0.16037736 0.22641509 0.22641509 0.2 0.22857143 0.17142857 0.24761905 0.19047619] mean value: 0.2070350404312668 key: test_accuracy value: [0.8125 0.8625 0.84810127 0.84810127 0.78481013 0.83544304 0.82278481 0.88607595 0.82278481 0.83544304] mean value: 0.8358544303797469 key: train_accuracy value: [0.88061798 0.86938202 0.86956522 0.87377279 0.87657784 0.86956522 0.87517532 0.86676017 0.87377279 0.87096774] mean value: 0.8726157082748948 key: test_roc_auc value: [0.47794118 0.61029412 0.53074866 0.49264706 0.45588235 0.52674129 0.48507463 0.625 0.48507463 0.49253731] mean value: 0.5181941229680473 key: train_roc_auc value: [0.614929 0.58471013 0.57689379 0.60661776 0.60826521 0.59259868 0.60770677 0.57913534 0.61476347 0.58948152] mean value: 0.5975101663181219 key: test_jcc value: [0. 0.21428571 0.07692308 0. 0. 0.07142857 0. 0.25 0. 0. ] mean value: 0.061263736263736254 key: train_jcc value: [0.22727273 0.16964286 0.15454545 0.21052632 0.21428571 0.18421053 0.21238938 0.15929204 0.22413793 0.17857143] mean value: 0.19348743708871313 MCC on Blind test: 0.0 MCC on Training: 0.06 Running classifier: 21 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=3))]) key: fit_time value: [0.11551499 0.05962539 0.11198974 0.16113281 0.13561177 0.14172912 0.17862511 0.11058831 0.15118599 0.10050035] mean value: 0.12665035724639892 key: score_time value: [0.01219535 0.01224613 0.01879191 0.02542734 0.02110434 0.01896882 0.02558017 0.01953602 0.02506495 0.01263213] mean value: 0.0191547155380249 key: test_mcc value: [-0.0829185 0. 0.28152101 0. -0.09288407 0.15630552 -0.0479188 0. -0.06820604 0. ] mean value: 0.014589911277934653 key: train_mcc value: [0.20955099 0.16094631 0.20358807 0.1859759 0.2964674 0.18118402 0.22644742 0.1191854 0.36669577 0.2225364 ] mean value: 0.21725776789750686 key: test_fscore value: [0. 0. 0.16666667 0. 0. 0.14285714 0. 0. 0. 0. ] mean value: 0.030952380952380953 key: train_fscore value: [0.15254237 0.11965812 0.13675214 0.14876033 0.22764228 0.10619469 0.16806723 0.07142857 0.36619718 0.15384615] mean value: 0.16510890618224486 key: test_precision value: [0. 0. 1. 0. 0. 0.5 0. 0. 0. 0. ] mean value: 0.15 key: train_precision value: [0.69230769 0.58333333 0.72727273 0.6 0.82352941 0.75 0.71428571 0.57142857 0.7027027 0.75 ] mean value: 0.6914860153095447 key: test_recall value: [0. 0. 0.09090909 0. 0. 0.08333333 0. 0. 0. 0. ] mean value: 0.017424242424242425 key: train_recall value: [0.08571429 0.06666667 0.0754717 0.08490566 0.13207547 0.05714286 0.0952381 0.03809524 0.24761905 0.08571429] mean value: 0.09686433063791554 key: test_accuracy value: [0.8125 0.85 0.87341772 0.86075949 0.81012658 0.84810127 0.83544304 0.84810127 0.82278481 0.84810127] mean value: 0.8409335443037975 key: train_accuracy value: [0.85955056 0.85533708 0.85834502 0.85553997 0.86676017 0.85834502 0.86115007 0.85413745 0.87377279 0.86115007] mean value: 0.8604088201459256 key: test_roc_auc value: [0.47794118 0.5 0.54545455 0.5 0.47058824 0.53420398 0.49253731 0.5 0.48507463 0.5 ] mean value: 0.5005799877617261 key: train_roc_auc value: [0.53956225 0.52921472 0.53526468 0.53751049 0.56356657 0.52692669 0.54432957 0.51658051 0.61476347 0.54039004] mean value: 0.5448108991696471 key: test_jcc value: [0. 0. 0.09090909 0. 0. 0.07692308 0. 0. 0. 0. ] mean value: 0.016783216783216783 key: train_jcc value: [0.08256881 0.06363636 0.0733945 0.08035714 0.12844037 0.05607477 0.09174312 0.03703704 0.22413793 0.08333333] mean value: 0.09207233632443254 MCC on Blind test: 0.0 MCC on Training: 0.01 Running classifier: 22 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.05275679 0.0272007 0.02761793 0.02686429 0.03093958 0.03177953 0.0315156 0.02909541 0.02627802 0.02765417] mean value: 0.031170201301574708 key: score_time value: [0.01355553 0.01285768 0.01313758 0.01292682 0.01427722 0.01320386 0.01330781 0.0138061 0.01445293 0.01322293] mean value: 0.013474845886230468 key: test_mcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_mcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_fscore value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_fscore value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_precision value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_precision value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_recall value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_recall value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: test_accuracy value: [0.85 0.85 0.86075949 0.86075949 0.86075949 0.84810127 0.84810127 0.84810127 0.84810127 0.84810127] mean value: 0.8522784810126582 key: train_accuracy value: [0.85252809 0.85252809 0.8513324 0.8513324 0.8513324 0.85273492 0.85273492 0.85273492 0.85273492 0.85273492] mean value: 0.8522727989031942 key: test_roc_auc value: [0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5] mean value: 0.5 key: train_roc_auc value: [0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5] mean value: 0.5 key: test_jcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 key: train_jcc value: [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] mean value: 0.0 MCC on Blind test: 0.0 MCC on Training: 0.0 Running classifier: 23 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.01862836 0.03147149 0.02750754 0.03110313 0.02771044 0.02616382 0.03023911 0.02887797 0.03055882 0.0301497 ] mean value: 0.02824103832244873 key: score_time value: [0.01075172 0.01213837 0.01242423 0.01233554 0.01256084 0.01260114 0.01265669 0.01264787 0.01255393 0.01297617] mean value: 0.012364649772644043 key: test_mcc value: [ 0.06424926 0.20008168 0.16794369 0.09347672 -0.13500744 0.26754663 0.10404981 0.23770803 -0.06820604 0.36445565] mean value: 0.12962979921715095 key: train_mcc value: [0.26997245 0.26720767 0.22115078 0.32929153 0.47548801 0.12762547 0.14496985 0.33781346 0.23192179 0.43739029] mean value: 0.28428312952964385 key: test_fscore value: [0.125 0.33333333 0.15384615 0.26666667 0. 0.15384615 0.28571429 0.37209302 0. 0.42105263] mean value: 0.21115522482413546 key: train_fscore value: [0.2 0.3654224 0.15254237 0.42130751 0.50909091 0.03738318 0.28532236 0.41935484 0.20634921 0.52293578] mean value: 0.31197085467240404 key: test_precision value: [0.25 0.20833333 0.5 0.17647059 0. 1. 0.16923077 0.25806452 0. 0.57142857] mean value: 0.31335277783570004 key: train_precision value: [0.8 0.23019802 0.75 0.28338762 0.71186441 1. 0.16666667 0.27659574 0.61904762 0.50442478] mean value: 0.5342184857887677 key: test_recall value: [0.08333333 0.83333333 0.09090909 0.54545455 0. 0.08333333 0.91666667 0.66666667 0. 0.33333333] mean value: 0.35530303030303034 key: train_recall value: [0.11428571 0.88571429 0.08490566 0.82075472 0.39622642 0.01904762 0.99047619 0.86666667 0.12380952 0.54285714] mean value: 0.4844743935309973 key: test_accuracy value: [0.825 0.5 0.86075949 0.58227848 0.75949367 0.86075949 0.30379747 0.65822785 0.82278481 0.86075949] mean value: 0.7033860759493671 key: train_accuracy value: [0.86516854 0.54634831 0.85974755 0.66479663 0.88639551 0.85553997 0.26928471 0.64656381 0.85974755 0.85413745] mean value: 0.7307730037663299 key: test_roc_auc value: [0.51960784 0.6372549 0.5381016 0.56684492 0.44117647 0.54166667 0.55534826 0.66169154 0.48507463 0.64427861] mean value: 0.5591045441242982 key: train_roc_auc value: [0.55467169 0.68667922 0.53998166 0.72915825 0.68410991 0.50952381 0.56760652 0.73760965 0.55532581 0.72537594] mean value: 0.6290042456686461 key: test_jcc value: [0.06666667 0.2 0.08333333 0.15384615 0. 0.08333333 0.16666667 0.22857143 0. 0.26666667] mean value: 0.12490842490842491 key: train_jcc value: [0.11111111 0.22355769 0.08256881 0.26687117 0.34146341 0.01904762 0.1664 0.26530612 0.11504425 0.35403727] mean value: 0.19454074474015254 MCC on Blind test: 0.0 MCC on Training: 0.13 Running classifier: 24 Model_name: XGBoost Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:463: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_CV['source_data'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:490: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_BT['source_data'] = 'BT' Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.1899569 0.1453321 0.1464262 0.14868951 0.14543128 0.143924 0.15011621 0.15970325 0.16059232 0.14094281] mean value: 0.15311145782470703 key: score_time value: [0.01253343 0.01150751 0.01220536 0.01123643 0.01159167 0.01245761 0.01246166 0.01260328 0.01160979 0.01122546] mean value: 0.011943221092224121 key: test_mcc value: [0.2415845 0.54492569 0.64628973 0.57419245 0.29875024 0.54569018 0.06312088 0.54569018 0.22397731 0.26754663] mean value: 0.3951767793242323 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.31578947 0.55555556 0.625 0.53333333 0.35294118 0.5 0.125 0.5 0.25 0.15384615] mean value: 0.3911465692889841 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.42857143 0.83333333 1. 1. 0.5 1. 0.25 1. 0.5 1. ] mean value: 0.7511904761904762 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.25 0.41666667 0.45454545 0.36363636 0.27272727 0.33333333 0.08333333 0.33333333 0.16666667 0.08333333] mean value: 0.27575757575757576 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.8375 0.9 0.92405063 0.91139241 0.86075949 0.89873418 0.82278481 0.89873418 0.84810127 0.86075949] mean value: 0.8762816455696202 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.59558824 0.70098039 0.72727273 0.68181818 0.61430481 0.66666667 0.51927861 0.66666667 0.56840796 0.54166667] mean value: 0.6282650916540293 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.1875 0.38461538 0.45454545 0.36363636 0.21428571 0.33333333 0.06666667 0.33333333 0.14285714 0.08333333] mean value: 0.25641067266067263 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.31 MCC on Training: 0.4 Extracting tts_split_name: sl Total cols in each df: CV df: 8 metaDF: 17 Adding column: Model_name Total cols in bts df: BT_df: 8 First proceeding to rowbind CV and BT dfs: Final output should have: 25 columns Combinig 2 using pd.concat by row ~ rowbind Checking Dims of df to combine: Dim of CV: (24, 8) Dim of BT: (24, 8) 8 Number of Common columns: 8 These are: ['source_data', 'Precision', 'JCC', 'MCC', 'Recall', 'Accuracy', 'F1', 'ROC_AUC'] Concatenating dfs with different resampling methods [WF]: Split type: sl No. of dfs combining: 2 PASS: 2 dfs successfully combined nrows in combined_df_wf: 48 ncols in combined_df_wf: 8 PASS: proceeding to merge metadata with CV and BT dfs Adding column: Model_name ========================================================= SUCCESS: Ran multiple classifiers ======================================================= ============================================================== Running several classification models (n): 24 List of models: ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('Gaussian NB', GaussianNB()) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('K-Nearest Neighbors', KNeighborsClassifier()) ('LDA', LinearDiscriminantAnalysis()) ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(cv=3, random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Multinomial', MultinomialNB()) ('Naive Bayes', BernoulliNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=12, random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Random Forest', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42)) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=3)) ('SVC', SVC(random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=12, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ================================================================ Running classifier: 1 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.51936412 0.54343009 0.54082537 0.53157043 0.51534152 0.53590775 0.53366518 0.53035378 0.51074672 0.52257133] mean value: 0.528377628326416 key: score_time value: [0.01773834 0.01631045 0.0174706 0.01823807 0.01695085 0.02133942 0.01806998 0.01635075 0.01744199 0.01618743] mean value: 0.017609786987304688 key: test_mcc value: [0.66485241 0.77860768 0.6000878 0.6889133 0.72063527 0.74824407 0.70368745 0.70368745 0.76419015 0.7658296 ] mean value: 0.7138735165472244 key: train_mcc value: [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.7s remaining: 1.4s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.8s remaining: 0.3s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.8s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. 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Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... .@’“n3ä?ˆEÊÀÓ­Ù?2@ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþÿÿÿÿÿÿÿÀ [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.7s remaining: 1.4s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.8s remaining: 0.3s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.8s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.7s remaining: 1.4s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.8s remaining: 0.3s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.8s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.7s remaining: 1.4s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.7s remaining: 0.2s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.9s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.7s remaining: 1.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.8s remaining: 0.3s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.8s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.7s remaining: 1.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.8s remaining: 0.3s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.8s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.7s remaining: 1.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.8s remaining: 0.3s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.9s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... ?ð?ð?ð?ð?ð?ð?ð?Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 6.5s remaining: 13.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 6.6s remaining: 13.2s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 6.8s remaining: 13.5s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 6.7s remaining: 13.5s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 6.8s remaining: 13.6s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 6.8s remaining: 2.3s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 6.8s remaining: 13.7s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 6.9s remaining: 13.7s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 6.9s remaining: 13.9s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 7.0s remaining: 14.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 7.1s remaining: 2.4s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 7.1s remaining: 2.4s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 7.2s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 7.2s remaining: 2.4s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 7.1s remaining: 14.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 7.2s remaining: 2.4s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 7.2s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 7.2s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 7.2s remaining: 2.4s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 7.2s remaining: 2.4s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 7.2s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 7.3s remaining: 2.4s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 7.3s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 7.3s remaining: 2.4s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 7.3s remaining: 2.4s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 7.3s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 7.4s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 7.4s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 7.4s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 7.4s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.8s remaining: 1.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.8s remaining: 0.3s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.9s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [0.82386955 0.84528655 0.84362222 0.82387752 0.82389491 0.82057736 0.83045346 0.83209865 0.8305093 0.84527238] mean value: 0.8319461908988496 key: test_fscore value: [0.816 0.89051095 0.8 0.84210526 0.86330935 0.87407407 0.85294118 0.85294118 0.88571429 0.88732394] mean value: 0.8564920220972498 key: train_fscore value: [0.91193416 0.92244224 0.92193919 0.91222313 0.91236691 0.91028807 0.91508656 0.91598023 0.91564292 0.92269737] mean value: 0.9160600784219733 key: test_precision value: [0.87931034 0.87142857 0.79411765 0.84848485 0.83333333 0.88059701 0.85294118 0.85294118 0.86111111 0.85135135] mean value: 0.8525616575462175 key: train_precision value: [0.91268534 0.92549669 0.92118227 0.90998363 0.908646 0.90953947 0.91584158 0.91598023 0.91042345 0.92118227] mean value: 0.915096093639234 key: test_recall value: [0.76119403 0.91044776 0.80597015 0.8358209 0.89552239 0.86764706 0.85294118 0.85294118 0.91176471 0.92647059] mean value: 0.8620719929762949 key: train_recall value: [0.91118421 0.91940789 0.92269737 0.91447368 0.91611842 0.91103789 0.91433278 0.91598023 0.92092257 0.92421746] mean value: 0.917037251799185 key: test_accuracy value: [0.82962963 0.88888889 0.8 0.84444444 0.85925926 0.87407407 0.85185185 0.85185185 0.88148148 0.88148148] mean value: 0.8562962962962963 key: train_accuracy value: [0.91193416 0.92263374 0.9218107 0.91193416 0.91193416 0.91028807 0.91522634 0.91604938 0.91522634 0.92263374] mean value: 0.9159670781893003 key: test_roc_auc value: [0.82912643 0.88904741 0.8000439 0.84438104 0.8595259 0.87412204 0.85184372 0.85184372 0.88125549 0.88114574] mean value: 0.8562335381913959 key: train_roc_auc value: [0.91193477 0.9226364 0.92180997 0.91193206 0.91193071 0.91028868 0.9152256 0.91604933 0.91523102 0.92263505] mean value: 0.915967359967051 key: test_jcc value: [0.68918919 0.80263158 0.66666667 0.72727273 0.75949367 0.77631579 0.74358974 0.74358974 0.79487179 0.79746835] mean value: 0.7501089258917373 key: train_jcc value: [0.83812405 0.856049 0.85518293 0.83861237 0.83885542 0.83534743 0.84346505 0.8449848 0.84441088 0.85648855] mean value: 0.8451520481397001 MCC on Blind test: 0.48 MCC on Training: 0.71 Running classifier: 2 Model_name: Bagging Classifier Model func: BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3))]) key: fit_time value: [0.92253113 0.89529419 1.05383253 0.94326973 1.06404448 1.03998542 1.05483007 0.96347451 1.01813078 1.10167122] mean value: 1.0057064056396485 key: score_time value: [0.06007075 0.07201171 0.06441665 0.06467247 0.06202507 0.088166 0.07528114 0.0522294 0.04331231 0.0575757 ] mean value: 0.0639761209487915 key: test_mcc value: [0.85184372 0.85532288 0.85225029 0.80746434 0.83790925 0.88188806 0.81163213 0.89640035 0.85225029 0.89721083] mean value: 0.8544172146420846 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.92537313 0.92857143 0.92647059 0.90225564 0.91970803 0.94029851 0.90909091 0.94814815 0.92537313 0.94736842] mean value: 0.9272657939512641 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.92537313 0.89041096 0.91304348 0.90909091 0.9 0.95454545 0.86666667 0.95522388 0.93939394 0.96923077] mean value: 0.922297919101809 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.92537313 0.97014925 0.94029851 0.89552239 0.94029851 0.92647059 0.95588235 0.94117647 0.91176471 0.92647059] mean value: 0.9333406496927129 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.92592593 0.92592593 0.92592593 0.9037037 0.91851852 0.94074074 0.9037037 0.94814815 0.92592593 0.94814815] mean value: 0.9266666666666665 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.92592186 0.9262511 0.92603161 0.90364355 0.91867867 0.94084723 0.90331431 0.94820018 0.92603161 0.94830992] mean value: 0.9267230026338893 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.86111111 0.86666667 0.8630137 0.82191781 0.85135135 0.88732394 0.83333333 0.90140845 0.86111111 0.9 ] mean value: 0.8647237474789087 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.69 MCC on Training: 0.85 Running classifier: 3 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.11514306 0.1173346 0.13836503 0.11887503 0.10557866 0.14604592 0.14640021 0.13722086 0.10446763 0.10659432] mean value: 0.1236025333404541 key: score_time value: [0.01009107 0.01035094 0.01022935 0.00962806 0.0108273 0.01059651 0.01049709 0.01018929 0.01012206 0.00977206] mean value: 0.010230374336242676 key: test_mcc value: [0.64571485 0.59516452 0.67428084 0.68945115 0.65199431 0.66087126 0.57168337 0.80825847 0.60141266 0.70629203] mean value: 0.6605123451239999 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.82608696 0.80821918 0.83333333 0.83969466 0.83333333 0.83687943 0.79432624 0.90225564 0.80851064 0.85915493] mean value: 0.8341794338491093 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.8028169 0.74683544 0.84615385 0.859375 0.77922078 0.80821918 0.76712329 0.92307692 0.78082192 0.82432432] mean value: 0.8137967600783942 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.85074627 0.88059701 0.82089552 0.82089552 0.89552239 0.86764706 0.82352941 0.88235294 0.83823529 0.89705882] mean value: 0.8577480245829674 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.82222222 0.79259259 0.83703704 0.84444444 0.82222222 0.82962963 0.78518519 0.9037037 0.8 0.85185185] mean value: 0.828888888888889 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.82243196 0.79323968 0.83691835 0.84427129 0.82276119 0.82934592 0.78489903 0.90386304 0.79971466 0.85151449] mean value: 0.8288959613696225 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.7037037 0.67816092 0.71428571 0.72368421 0.71428571 0.7195122 0.65882353 0.82191781 0.67857143 0.75308642] mean value: 0.7166031643419086 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.01 MCC on Training: 0.66 Running classifier: 4 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.01328325 0.0131731 0.01307535 0.01328921 0.01327753 0.0133431 0.01346135 0.01299191 0.01352215 0.01318574] mean value: 0.013260269165039062 key: score_time value: [0.00907612 0.009027 0.00898051 0.00895 0.00900197 0.00907826 0.00910163 0.00897551 0.00896668 0.00896263] mean value: 0.00901203155517578 key: test_mcc value: [0.58551044 0.66124225 0.54072449 0.70393163 0.65199431 0.61882494 0.52589991 0.63114196 0.57045654 0.66255684] mean value: 0.6152283311133807 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.79411765 0.83453237 0.76691729 0.84848485 0.83333333 0.81944444 0.76470588 0.82269504 0.78518519 0.83916084] mean value: 0.810857688281521 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.7826087 0.80555556 0.77272727 0.86153846 0.77922078 0.77631579 0.76470588 0.79452055 0.79104478 0.8 ] mean value: 0.7928237760585477 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.80597015 0.86567164 0.76119403 0.8358209 0.89552239 0.86764706 0.76470588 0.85294118 0.77941176 0.88235294] mean value: 0.8311237928007025 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.79259259 0.82962963 0.77037037 0.85185185 0.82222222 0.80740741 0.76296296 0.81481481 0.78518519 0.82962963] mean value: 0.8066666666666666 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.79269096 0.82989464 0.7703029 0.85173398 0.82276119 0.80695786 0.76294996 0.81453029 0.78522827 0.82923617] mean value: 0.8066286215978931 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.65853659 0.71604938 0.62195122 0.73684211 0.71428571 0.69411765 0.61904762 0.69879518 0.64634146 0.72289157] mean value: 0.6828858483651998 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.12 MCC on Training: 0.62 Running classifier: 5 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.23968577 0.23398972 0.26665902 0.25621867 0.21928549 0.21817708 0.21444821 0.21129966 0.21523666 0.22073054] mean value: 0.22957308292388917 key: score_time value: [0.0207293 0.02213001 0.02468061 0.02260494 0.01982379 0.01937437 0.01949191 0.01963735 0.02009964 0.02012849] mean value: 0.020870041847229005 key: test_mcc value: [0.7333187 0.85184372 0.88188806 0.86747328 0.85532288 0.88147498 0.88147498 0.91274805 0.85225029 0.88188806] mean value: 0.8599682993286122 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.86567164 0.92537313 0.94117647 0.93129771 0.92857143 0.94117647 0.94117647 0.95454545 0.92537313 0.94029851] mean value: 0.9294660422715701 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.86567164 0.92537313 0.92753623 0.953125 0.89041096 0.94117647 0.94117647 0.984375 0.93939394 0.95454545] mean value: 0.9322784302023436 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.86567164 0.92537313 0.95522388 0.91044776 0.97014925 0.94117647 0.94117647 0.92647059 0.91176471 0.92647059] mean value: 0.9273924495171203 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.86666667 0.92592593 0.94074074 0.93333333 0.92592593 0.94074074 0.94074074 0.95555556 0.92592593 0.94074074] mean value: 0.9296296296296298 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.86665935 0.92592186 0.94084723 0.93316506 0.9262511 0.94073749 0.94073749 0.95577261 0.92603161 0.94084723] mean value: 0.9296971027216857 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.76315789 0.86111111 0.88888889 0.87142857 0.86666667 0.88888889 0.88888889 0.91304348 0.86111111 0.88732394] mean value: 0.869050944364381 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.27 MCC on Training: 0.86 Running classifier: 6 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [2.39187002 2.42430186 2.32788944 2.31680012 2.31642866 2.33518243 2.33661079 2.37529707 2.41691279 2.31971765] mean value: 2.356101083755493 key: score_time value: [0.00989056 0.01004004 0.01007342 0.00999713 0.00997639 0.0104301 0.00971818 0.0098896 0.01029921 0.01013875] mean value: 0.010045337677001952 key: test_mcc value: [0.85218556 0.80972479 0.79297477 0.80813257 0.8394213 0.88307769 0.80813257 0.79297477 0.85513596 0.85184372] mean value: 0.8293603709420327 key: train_mcc value: [0.9671256 0.95885957 0.97039155 0.9637913 0.9670835 0.96549585 0.97037553 0.96709895 0.97366253 0.97037553] mean value: 0.9674259910013621 key: test_fscore value: [0.92424242 0.90647482 0.89705882 0.90076336 0.92086331 0.93939394 0.90647482 0.89552239 0.92957746 0.92647059] mean value: 0.9146841936668416 key: train_fscore value: [0.98347107 0.97938994 0.98514851 0.98187809 0.98352554 0.98260149 0.98514851 0.98347107 0.98682043 0.98514851] mean value: 0.9836603179625657 key: test_precision value: [0.93846154 0.875 0.88405797 0.921875 0.88888889 0.96875 0.88732394 0.90909091 0.89189189 0.92647059] mean value: 0.9091810731244985 key: train_precision value: [0.98837209 0.98181818 0.9884106 0.98349835 0.98514851 0.98833333 0.98677686 0.986733 0.98682043 0.98677686] mean value: 0.986268821789045 key: test_recall value: [0.91044776 0.94029851 0.91044776 0.88059701 0.95522388 0.91176471 0.92647059 0.88235294 0.97058824 0.92647059] mean value: 0.9214661984196664 key: train_recall value: [0.97861842 0.97697368 0.98190789 0.98026316 0.98190789 0.97693575 0.98352554 0.98023064 0.98682043 0.98352554] mean value: 0.9810708943900114 key: test_accuracy value: [0.92592593 0.9037037 0.8962963 0.9037037 0.91851852 0.94074074 0.9037037 0.8962963 0.92592593 0.92592593] mean value: 0.9140740740740739 key: train_accuracy value: [0.98353909 0.97942387 0.98518519 0.981893 0.98353909 0.98271605 0.98518519 0.98353909 0.98683128 0.98518519] mean value: 0.9837037037037039 key: test_roc_auc value: [0.92581212 0.90397278 0.89640035 0.9035338 0.91878841 0.94095698 0.9035338 0.89640035 0.92559263 0.92592186] mean value: 0.9140913081650572 key: train_roc_auc value: [0.98354315 0.97942589 0.98518788 0.98189435 0.98354044 0.9827113 0.98518382 0.98353637 0.98683127 0.98518382] mean value: 0.9837038281453221 key: test_jcc value: [0.85915493 0.82894737 0.81333333 0.81944444 0.85333333 0.88571429 0.82894737 0.81081081 0.86842105 0.8630137 ] mean value: 0.8431120625317494 key: train_jcc value: [0.96747967 0.95961228 0.97073171 0.96440129 0.96758509 0.96579805 0.97073171 0.96747967 0.97398374 0.97073171] mean value: 0.9678534918491634 MCC on Blind test: 0.49 MCC on Training: 0.83 Running classifier: 7 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.01369429 0.01296759 0.01329017 0.01323533 0.0129261 0.01289463 0.0130713 0.01291943 0.01273322 0.01322913] mean value: 0.013096117973327636 key: score_time value: [0.00996614 0.0093441 0.00954723 0.00947261 0.00945783 0.00934815 0.009444 0.00955868 0.009624 0.00929284] mean value: 0.009505558013916015 key: test_mcc value: [0.408352 0.36247192 0.35115512 0.41256764 0.34293974 0.37119084 0.22215274 0.3954974 0.47941251 0.43961571] mean value: 0.3785355633086219 key: train_mcc value: [0.41522303 0.39271753 0.41129119 0.42501453 0.40927073 0.43107725 0.41669444 0.43949707 0.39043782 0.41332437] mean value: 0.4144547979742897 key: test_fscore value: [0.71014493 0.71052632 0.69014085 0.72222222 0.69798658 0.71523179 0.65806452 0.71724138 0.76315789 0.73611111] mean value: 0.712082757716636 key: train_fscore value: [0.73013493 0.71689498 0.72307692 0.73423423 0.72645064 0.73564955 0.72796353 0.74101796 0.71666667 0.72836719] mean value: 0.7280456604337081 key: test_precision value: [0.69014085 0.63529412 0.65333333 0.67532468 0.63414634 0.65060241 0.5862069 0.67532468 0.69047619 0.69736842] mean value: 0.6588217905882681 key: train_precision value: [0.6707989 0.66713881 0.67919075 0.67541436 0.67037552 0.67921897 0.67559944 0.67901235 0.66339411 0.67036011] mean value: 0.6730503315539869 key: test_recall value: [0.73134328 0.80597015 0.73134328 0.7761194 0.7761194 0.79411765 0.75 0.76470588 0.85294118 0.77941176] mean value: 0.7762071992976295 key: train_recall value: [0.80098684 0.77467105 0.77302632 0.80427632 0.79276316 0.80230643 0.78912685 0.815486 0.77924217 0.79736409] mean value: 0.7929249219630625 key: test_accuracy value: [0.7037037 0.67407407 0.67407407 0.7037037 0.66666667 0.68148148 0.60740741 0.6962963 0.73333333 0.71851852] mean value: 0.6859259259259259 key: train_accuracy value: [0.7037037 0.69382716 0.7037037 0.70864198 0.70123457 0.71193416 0.70534979 0.71522634 0.69218107 0.70288066] mean value: 0.7038683127572017 key: test_roc_auc value: [0.70390694 0.6750439 0.67449517 0.70423617 0.66747147 0.68064091 0.60634328 0.69578578 0.73244074 0.71806409] mean value: 0.6858428446005268 key: train_roc_auc value: [0.70362357 0.69376057 0.7036466 0.7085632 0.70115917 0.71200848 0.70541869 0.71530879 0.69225267 0.70295836] mean value: 0.7038700088875401 key: test_jcc value: [0.5505618 0.55102041 0.52688172 0.56521739 0.53608247 0.55670103 0.49038462 0.55913978 0.61702128 0.58241758] mean value: 0.5535428082149348 key: train_jcc value: [0.57497048 0.55871886 0.56626506 0.58007117 0.5704142 0.5818399 0.57228196 0.58858502 0.55844156 0.57278107] mean value: 0.5724369286238487 MCC on Blind test: 0.15 MCC on Training: 0.38 Running classifier: 8 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.91589069 0.8546176 0.79263043 0.77317333 0.89855886 1.04416418 0.81257248 0.88716149 0.84195924 0.76512837] mean value: 0.8585856676101684 key: score_time value: [0.04315257 0.05235982 0.05433631 0.04881597 0.05669451 0.02936745 0.02946472 0.06843376 0.05084777 0.02877808] mean value: 0.04622509479522705 key: test_mcc value: [0.65935031 0.74818882 0.64478597 0.70629203 0.74551484 0.77978844 0.79388665 0.7632332 0.80813257 0.73358241] mean value: 0.7382755244964454 key: train_mcc value: [0.88972316 0.90623205 0.89964718 0.8962973 0.89632588 0.88643095 0.89968837 0.91611094 0.90453184 0.89968837] mean value: 0.8994676039986345 key: test_fscore value: [0.82962963 0.87218045 0.82352941 0.84375 0.87671233 0.89361702 0.9 0.88405797 0.90647482 0.86956522] mean value: 0.8699516851115557 key: train_fscore value: [0.94476505 0.9533933 0.95012265 0.94823336 0.94840295 0.9433032 0.95012265 0.95823096 0.95230263 0.95012265] mean value: 0.9498999391725012 key: test_precision value: [0.82352941 0.87878788 0.8115942 0.8852459 0.81012658 0.8630137 0.875 0.87142857 0.88732394 0.85714286] mean value: 0.8563193048232499 key: train_precision value: [0.94710744 0.94796748 0.94471545 0.94745484 0.94453507 0.94098361 0.94318182 0.95276873 0.95073892 0.94318182] mean value: 0.9462635171080691 key: test_recall value: [0.8358209 0.86567164 0.8358209 0.80597015 0.95522388 0.92647059 0.92647059 0.89705882 0.92647059 0.88235294] mean value: 0.8857330992098331 key: train_recall value: [0.94243421 0.95888158 0.95559211 0.94901316 0.95230263 0.94563427 0.95716639 0.96375618 0.9538715 0.95716639] mean value: 0.9535818412381861 key: test_accuracy value: [0.82962963 0.87407407 0.82222222 0.85185185 0.86666667 0.88888889 0.8962963 0.88148148 0.9037037 0.86666667] mean value: 0.8681481481481482 key: train_accuracy value: [0.94485597 0.95308642 0.94979424 0.94814815 0.94814815 0.94320988 0.94979424 0.95802469 0.95226337 0.94979424] mean value: 0.9497119341563787 key: test_roc_auc value: [0.82967515 0.87401229 0.82232221 0.85151449 0.86731782 0.88860843 0.89607112 0.88136523 0.9035338 0.8665496 ] mean value: 0.8680970149253732 key: train_roc_auc value: [0.94485796 0.95308165 0.94978946 0.94814744 0.94814473 0.94321187 0.9498003 0.9580294 0.9522647 0.9498003 ] mean value: 0.9497127807162057 key: test_jcc value: [0.70886076 0.77333333 0.7 0.72972973 0.7804878 0.80769231 0.81818182 0.79220779 0.82894737 0.76923077] mean value: 0.7708671683168522 key: train_jcc value: [0.8953125 0.9109375 0.90498442 0.9015625 0.90186916 0.89269051 0.90498442 0.91981132 0.90894819 0.90498442] mean value: 0.9046084958543024 MCC on Blind test: 0.16 MCC on Training: 0.74 Running classifier: 9 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.01620603 0.01317787 0.01233935 0.01189613 0.01266837 0.0114007 0.01267672 0.01309896 0.01241326 0.01280022] mean value: 0.01286776065826416 key: score_time value: [0.03203154 0.01888371 0.01782179 0.01664829 0.01689887 0.01634312 0.01697803 0.02227163 0.01682615 0.02001143] mean value: 0.019471454620361327 key: test_mcc value: [0.6000878 0.64571485 0.57590322 0.61479368 0.6985636 0.79388665 0.73606081 0.66087126 0.6923719 0.73358241] mean value: 0.6751836173247104 key: train_mcc value: [0.78305442 0.78981209 0.77865667 0.79150735 0.79001677 0.77834255 0.80282629 0.79054896 0.78832913 0.77564064] mean value: 0.7868734863367151 key: test_fscore value: [0.8 0.82608696 0.7972028 0.80597015 0.85517241 0.9 0.87323944 0.83687943 0.85314685 0.86956522] mean value: 0.841726325655336 key: train_fscore value: [0.89303079 0.89660743 0.89156627 0.89749798 0.89694042 0.890865 0.9025974 0.8974359 0.89588378 0.89013633] mean value: 0.8952561291905938 key: test_precision value: [0.79411765 0.8028169 0.75 0.80597015 0.79487179 0.875 0.83783784 0.80821918 0.81333333 0.85714286] mean value: 0.813930969898902 key: train_precision value: [0.88019169 0.88095238 0.87127159 0.88114105 0.8785489 0.87460317 0.8896 0.87363495 0.87816456 0.8671875 ] mean value: 0.8775295778621282 key: test_recall value: [0.80597015 0.85074627 0.85074627 0.80597015 0.92537313 0.92647059 0.91176471 0.86764706 0.89705882 0.88235294] mean value: 0.8724100087796313 key: train_recall value: [0.90625 0.91282895 0.91282895 0.91447368 0.91611842 0.907743 0.91598023 0.92257002 0.91433278 0.91433278] mean value: 0.913745881383855 key: test_accuracy value: [0.8 0.82222222 0.78518519 0.80740741 0.84444444 0.8962963 0.86666667 0.82962963 0.84444444 0.86666667] mean value: 0.8362962962962962 key: train_accuracy value: [0.89135802 0.89465021 0.88888889 0.89547325 0.89465021 0.88888889 0.90123457 0.89465021 0.89382716 0.8872428 ] mean value: 0.8930864197530864 key: test_roc_auc value: [0.8000439 0.82243196 0.78566725 0.80739684 0.84503951 0.89607112 0.86633011 0.82934592 0.8440518 0.8665496 ] mean value: 0.8362928007023704 key: train_roc_auc value: [0.89134576 0.89463523 0.88886917 0.8954576 0.89463252 0.88890439 0.90124669 0.89467317 0.89384402 0.88726508] mean value: 0.8930873634353593 key: test_jcc value: [0.66666667 0.7037037 0.6627907 0.675 0.74698795 0.81818182 0.775 0.7195122 0.74390244 0.76923077] mean value: 0.7280976241410947 key: train_jcc value: [0.80673499 0.81259151 0.80434783 0.81405564 0.81313869 0.803207 0.82248521 0.81395349 0.81140351 0.80202312] mean value: 0.81039409725629 MCC on Blind test: 0.19 MCC on Training: 0.68 Running classifier: 10 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.06695652 0.07838464 0.07419586 0.07451487 0.07864332 0.09096456 0.0777142 0.06931114 0.08774614 0.06798577] mean value: 0.07664170265197753 key: score_time value: [0.019629 0.01244354 0.01251912 0.01277423 0.01945305 0.02109051 0.01264763 0.01298904 0.01317143 0.01316071] mean value: 0.014987826347351074 key: test_mcc value: [0.64463005 0.70368745 0.65927554 0.61497927 0.67912076 0.79586428 0.67428084 0.66124225 0.62971905 0.73640569] mean value: 0.6799205182318936 key: train_mcc value: [0.78467424 0.75848469 0.78014626 0.78653035 0.78014626 0.75999846 0.75847097 0.77131631 0.77333988 0.7605889 ] mean value: 0.771369631824787 key: test_fscore value: [0.81818182 0.85074627 0.82706767 0.8030303 0.84507042 0.89230769 0.84057971 0.82442748 0.81481481 0.86153846] mean value: 0.8377764641298908 key: train_fscore value: [0.89074229 0.87698745 0.88739496 0.89112228 0.88739496 0.87792642 0.87678122 0.88445553 0.88422819 0.87668919] mean value: 0.8833722477125697 key: test_precision value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( [0.83076923 0.85074627 0.83333333 0.81538462 0.8 0.93548387 0.82857143 0.85714286 0.82089552 0.90322581] mean value: 0.8475552933665597 key: train_precision value: [0.9035533 0.89267462 0.90721649 0.90784983 0.90721649 0.89134126 0.89249147 0.89261745 0.9008547 0.89948007] mean value: 0.8995295679016438 key: test_recall value: [0.80597015 0.85074627 0.82089552 0.79104478 0.89552239 0.85294118 0.85294118 0.79411765 0.80882353 0.82352941] mean value: 0.8296532045654083 key: train_recall value: [0.87828947 0.86184211 0.86842105 0.875 0.86842105 0.86490939 0.8616145 0.87644152 0.86820428 0.85502471] mean value: 0.867816808289257 key: test_accuracy value: [0.82222222 0.85185185 0.82962963 0.80740741 0.83703704 0.8962963 0.83703704 0.82962963 0.81481481 0.86666667] mean value: 0.8392592592592593 key: train_accuracy value: [0.89218107 0.87901235 0.88971193 0.89300412 0.88971193 0.87983539 0.87901235 0.88559671 0.88641975 0.87983539] mean value: 0.885432098765432 key: test_roc_auc value: [0.82210272 0.85184372 0.82956541 0.80728709 0.83746708 0.89661984 0.83691835 0.82989464 0.81485953 0.86698859] mean value: 0.8393546971027217 key: train_roc_auc value: [0.89219251 0.87902649 0.88972947 0.89301895 0.88972947 0.87982312 0.87899804 0.88558918 0.88640477 0.87981499] mean value: 0.8854326985606521 key: test_jcc value: [0.69230769 0.74025974 0.70512821 0.67088608 0.73170732 0.80555556 0.725 0.7012987 0.6875 0.75675676] mean value: 0.721640004432919 key: train_jcc value: [0.80300752 0.78092399 0.79758308 0.80362538 0.79758308 0.78241431 0.78059701 0.7928465 0.7924812 0.78045113] mean value: 0.7911513204142682 MCC on Blind test: 0.33 MCC on Training: 0.68 Running classifier: 11 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.09720063 0.06788898 0.04945302 0.04924035 0.0506742 0.04976702 0.05116343 0.04988146 0.05132508 0.0518868 ] mean value: 0.05684809684753418 key: score_time value: [0.01253343 0.01260495 0.01254368 0.01248527 0.01267505 0.01250625 0.01244974 0.01261544 0.0127306 0.01264954] mean value: 0.012579393386840821 key: test_mcc value: [0.52589991 0.6493057 0.51175061 0.48156277 0.50023041 0.67856135 0.53125495 0.60044008 0.60141266 0.67510062] mean value: 0.5755519066083238 key: train_mcc value: [0.65152589 0.64691832 0.64351777 0.66033981 0.65330757 0.65173438 0.64194977 0.63754796 0.64175264 0.62936906] mean value: 0.64579631519795 key: test_fscore value: [0.76119403 0.83098592 0.75912409 0.74074074 0.76056338 0.84722222 0.78082192 0.8057554 0.80851064 0.84285714] mean value: 0.7937775470826285 key: train_fscore value: [0.82903226 0.82758621 0.82570281 0.83453237 0.8302494 0.82930757 0.82475884 0.8236233 0.82419355 0.81977671] mean value: 0.8268763024780463 key: test_precision value: [0.76119403 0.78666667 0.74285714 0.73529412 0.72 0.80263158 0.73076923 0.78873239 0.78082192 0.81944444] mean value: 0.7668411523357075 key: train_precision value: [0.81329114 0.80751174 0.80690738 0.8118196 0.81259843 0.81102362 0.80533752 0.79876161 0.80726698 0.79443586] mean value: 0.8068953867513207 key: test_recall value: [0.76119403 0.88059701 0.7761194 0.74626866 0.80597015 0.89705882 0.83823529 0.82352941 0.83823529 0.86764706] mean value: 0.8234855136084283 key: train_recall value: [0.84539474 0.84868421 0.84539474 0.85855263 0.84868421 0.84843493 0.84514003 0.85008237 0.84184514 0.84678748] mean value: 0.8479000476892397 key: test_accuracy value: [0.76296296 0.82222222 0.75555556 0.74074074 0.74814815 0.83703704 0.76296296 0.8 0.8 0.83703704] mean value: 0.7866666666666666 key: train_accuracy value: [0.8255144 0.82304527 0.82139918 0.82962963 0.82633745 0.8255144 0.82057613 0.818107 0.82057613 0.81399177] mean value: 0.822469135802469 key: test_roc_auc value: [0.76294996 0.82265145 0.75570676 0.74078139 0.74857331 0.83658911 0.76240123 0.79982441 0.79971466 0.8368086 ] mean value: 0.7866000877963126 key: train_roc_auc value: [0.82549803 0.82302415 0.82137941 0.82960581 0.82631904 0.82553325 0.82059633 0.81813329 0.82059362 0.81401874] mean value: 0.8224701671291077 key: test_jcc value: [0.61445783 0.71084337 0.61176471 0.58823529 0.61363636 0.73493976 0.64044944 0.6746988 0.67857143 0.72839506] mean value: 0.659599205117458 key: train_jcc value: [0.70798898 0.70588235 0.70314637 0.71604938 0.70976616 0.70839065 0.70177839 0.70013569 0.70096022 0.69459459] mean value: 0.704869278506234 MCC on Blind test: 0.37 MCC on Training: 0.58 Running classifier: 12 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(cv=3, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(cv=3, random_state=42))]) key: fit_time value: [0.63149452 0.85307574 0.6737144 0.66303825 0.8284235 0.64028764 0.76838946 0.7965374 0.6632359 0.66877055] mean value: 0.7186967372894287 key: score_time value: [0.01248026 0.01262474 0.01246929 0.01248693 0.01251054 0.01246285 0.01242518 0.01248693 0.01262403 0.01253486] mean value: 0.012510561943054199 key: test_mcc value: [0.65978079 0.70406149 0.61604449 0.58532848 0.66550783 0.77787533 0.68898156 0.6769471 0.67405619 0.75033178] mean value: 0.6798915044362237 key: train_mcc value: [0.79455443 0.78483716 0.79481476 0.79107917 0.79311884 0.78627621 0.77485872 0.78642861 0.77465887 0.76974723] mean value: 0.7850374004363883 key: test_fscore value: [0.82442748 0.85294118 0.8115942 0.78787879 0.83916084 0.88888889 0.84444444 0.83076923 0.83823529 0.87022901] mean value: 0.8388569353178594 key: train_fscore value: [0.89574646 0.89037657 0.89522213 0.89460581 0.89447236 0.89148581 0.88535565 0.89112228 0.88592839 0.88333333] mean value: 0.8907648787055635 key: test_precision value: [0.84375 0.84057971 0.78873239 0.8 0.78947368 0.89552239 0.85074627 0.87096774 0.83823529 0.9047619 ] mean value: 0.8422769386253105 key: train_precision value: [0.90862944 0.90630324 0.91282051 0.90284757 0.9112628 0.9035533 0.89965986 0.90630324 0.8956229 0.89376054] mean value: 0.9040763396553384 key: test_recall value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( [0.80597015 0.86567164 0.8358209 0.7761194 0.89552239 0.88235294 0.83823529 0.79411765 0.83823529 0.83823529] mean value: 0.8370280948200175 key: train_recall value: [0.88322368 0.875 0.87828947 0.88651316 0.87828947 0.87973641 0.87149918 0.87644152 0.87644152 0.87314662] mean value: 0.877858102835342 key: test_accuracy value: [0.82962963 0.85185185 0.80740741 0.79259259 0.82962963 0.88888889 0.84444444 0.83703704 0.83703704 0.87407407] mean value: 0.8392592592592593 key: train_accuracy value: [0.89711934 0.89218107 0.89711934 0.89547325 0.8962963 0.89300412 0.8872428 0.89300412 0.8872428 0.88477366] mean value: 0.8923456790123456 key: test_roc_auc value: [0.82945566 0.85195347 0.80761633 0.79247147 0.83011414 0.88893766 0.84449078 0.83735733 0.83702809 0.87434153] mean value: 0.8393766461808605 key: train_roc_auc value: [0.89713079 0.89219522 0.89713485 0.89548063 0.89631113 0.8929932 0.88722985 0.89299049 0.88723392 0.8847641 ] mean value: 0.8923464189716466 key: test_jcc value: [0.7012987 0.74358974 0.68292683 0.65 0.72289157 0.8 0.73076923 0.71052632 0.72151899 0.77027027] mean value: 0.7233791644592544 key: train_jcc value: [0.81117825 0.80241327 0.81031866 0.80930931 0.80909091 0.80421687 0.79429429 0.80362538 0.79521674 0.79104478] mean value: 0.8030708460711429 MCC on Blind test: 0.3 MCC on Training: 0.68 Running classifier: 13 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [2.26393604 4.45876098 3.29899788 3.00294185 3.43620038 4.52883267 3.50806284 2.94178057 3.70398021 3.57354641] mean value: 3.4717039823532105 key: score_time value: [0.01284528 0.01279831 0.0128088 0.01270628 0.01307011 0.01489401 0.01277161 0.01336217 0.01293015 0.01297283] mean value: 0.013115954399108887 key: test_mcc value: [0.67136005 0.79288398 0.68537472 0.66255684 0.66856802 0.77121954 0.6933147 0.73472591 0.74879186 0.7112284 ] mean value: 0.7140024026359949 key: train_mcc value: [0.83585065 0.93617051 0.87389263 0.86012759 0.8619546 0.89843207 0.84711128 0.81932845 0.89465013 0.85633481] mean value: 0.8683852723432611 key: test_fscore value: [0.80991736 0.89393939 0.84931507 0.81889764 0.84137931 0.89041096 0.85526316 0.86363636 0.87769784 0.8630137 ] mean value: 0.8563470786736515 key: train_fscore value: [0.91389599 0.96747289 0.93809524 0.92699491 0.93170732 0.94975689 0.9251379 0.90520922 0.94728171 0.92946708] mean value: 0.9335019161948181 key: test_precision value: [0.90740741 0.90769231 0.78481013 0.86666667 0.78205128 0.83333333 0.77380952 0.890625 0.85915493 0.80769231] mean value: 0.8413242884812572 key: train_precision value: [0.94867257 0.98138748 0.90644172 0.95789474 0.92122186 0.93460925 0.88670695 0.93971631 0.94728171 0.88639761] mean value: 0.9310330197617338 key: test_recall value: [0.73134328 0.88059701 0.92537313 0.7761194 0.91044776 0.95588235 0.95588235 0.83823529 0.89705882 0.92647059] mean value: 0.8797410008779633 key: train_recall value: [0.88157895 0.95394737 0.97203947 0.89802632 0.94243421 0.96540362 0.96705107 0.87314662 0.94728171 0.97693575] mean value: 0.9377845096679094 key: test_accuracy value: [0.82962963 0.8962963 0.83703704 0.82962963 0.82962963 0.88148148 0.83703704 0.86666667 0.87407407 0.85185185] mean value: 0.8533333333333333 key: train_accuracy value: [0.91687243 0.96790123 0.93580247 0.92921811 0.9308642 0.94897119 0.9218107 0.90864198 0.9473251 0.92592593] mean value: 0.9333333333333332 key: test_roc_auc value: [0.82890694 0.89618086 0.83768657 0.82923617 0.83022388 0.88092625 0.83615013 0.86687884 0.87390255 0.851295 ] mean value: 0.8531387181738367 key: train_roc_auc value: [0.9169015 0.96791273 0.93577262 0.9292438 0.93085467 0.94898471 0.9218479 0.90861279 0.94732507 0.92596787] mean value: 0.933342365386283 key: test_jcc value: [0.68055556 0.80821918 0.73809524 0.69333333 0.72619048 0.80246914 0.74712644 0.76 0.78205128 0.75903614] mean value: 0.7497076780470469 key: train_jcc value: [0.84144427 0.93699515 0.88340807 0.86392405 0.87214612 0.90432099 0.86070381 0.82683307 0.89984351 0.8682284 ] mean value: 0.8757847447463133 MCC on Blind test: 0.16 MCC on Training: 0.71 Running classifier: 14 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.0169971 0.01691055 0.01724744 0.01823807 0.01686406 0.01717257 0.01709795 0.01691937 0.01729345 0.01704478] mean value: 0.01717853546142578 key: score_time value: [0.01259041 0.01247883 0.01256204 0.01259017 0.01274514 0.01281595 0.0126462 0.01266885 0.01255846 0.01241255] mean value: 0.01260685920715332 key: test_mcc value: [0.33384589 0.23588326 0.27862556 0.24630915 0.39860785 0.32662524 0.29417308 0.46722506 0.34906659 0.42542573] mean value: 0.335578741284073 key: train_mcc value: [0.33564508 0.31688222 0.36125978 0.32554386 0.39366181 0.35247469 0.33546921 0.33266069 0.3200457 0.35035975] mean value: 0.342400278816685 key: test_fscore value: [0.67153285 0.64864865 0.66206897 0.63829787 0.71724138 0.69736842 0.68 0.74285714 0.69014085 0.73103448] mean value: 0.6879190604270807 key: train_fscore value: [0.69007634 0.68429003 0.69953775 0.68877167 0.71273292 0.69724771 0.68865031 0.68854962 0.68437026 0.6953185 ] mean value: 0.6929545086376594 key: test_precision value: [0.65714286 0.59259259 0.61538462 0.60810811 0.66666667 0.63095238 0.62195122 0.72222222 0.66216216 0.68831169] mean value: 0.6465494513055489 key: train_precision value: [0.64387464 0.63268156 0.65797101 0.63560501 0.675 0.65049929 0.64418938 0.64153627 0.63431786 0.65086207] mean value: 0.6466537103617551 key: test_recall value: [0.68656716 0.71641791 0.71641791 0.67164179 0.7761194 0.77941176 0.75 0.76470588 0.72058824 0.77941176] mean value: 0.7361281826163302 key: train_recall value: [0.74342105 0.74506579 0.74671053 0.75164474 0.75493421 0.75123558 0.73970346 0.74299835 0.74299835 0.74629325] mean value: 0.7465005310847135 key: test_accuracy value: [0.66666667 0.61481481 0.63703704 0.62222222 0.6962963 0.65925926 0.64444444 0.73333333 0.67407407 0.71111111] mean value: 0.6659259259259259 key: train_accuracy value: [0.66584362 0.65596708 0.67901235 0.6600823 0.69547325 0.67407407 0.66584362 0.66419753 0.65761317 0.67325103] mean value: 0.669135802469136 key: test_roc_auc value: [0.66681299 0.6155619 0.63762072 0.6225856 0.69688323 0.6583626 0.64365672 0.73309921 0.67372695 0.7106014 ] mean value: 0.6658911325724319 key: train_roc_auc value: [0.66577972 0.65589369 0.67895658 0.66000688 0.69542427 0.67413753 0.66590436 0.66426233 0.65768339 0.6733111 ] mean value: 0.6691359847827971 key: test_jcc value: [0.50549451 0.48 0.49484536 0.46875 0.55913978 0.53535354 0.51515152 0.59090909 0.52688172 0.57608696] mean value: 0.5252612469631474 key: train_jcc value: [0.52680653 0.52009185 0.53791469 0.52528736 0.55367913 0.53521127 0.5251462 0.5250291 0.52018454 0.53294118] mean value: 0.5302291845926744 MCC on Blind test: 0.15 MCC on Training: 0.34 Running classifier: 15 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.01787853 0.01822114 0.02162886 0.01865625 0.01763678 0.0176754 0.01771879 0.01774502 0.01817894 0.01771808] mean value: 0.01830577850341797 key: score_time value: [0.01277041 0.01291919 0.0128305 0.01313543 0.01266479 0.0127182 0.01280618 0.01275563 0.01284027 0.01274729] mean value: 0.0128187894821167 key: test_mcc value: [0.42230026 0.57215273 0.49842604 0.3968487 0.5382977 0.39918706 0.51395993 0.3851892 0.45728771 0.58429859] mean value: 0.47679479233874245 key: train_mcc value: [0.51459113 0.48180094 0.50920952 0.51434476 0.5117538 0.52798156 0.5264981 0.49594406 0.51114765 0.52128521] mean value: 0.5114556738651476 key: test_fscore value: [0.71111111 0.79136691 0.76821192 0.72727273 0.78378378 0.72483221 0.77922078 0.72 0.74829932 0.80794702] mean value: 0.7562045782753565 key: train_fscore value: [0.77112135 0.75815011 0.77211394 0.77529762 0.77371173 0.78092399 0.77961019 0.76576577 0.77040427 0.7771943 ] mean value: 0.7724293277563314 key: test_precision value: [0.70588235 0.76388889 0.69047619 0.64367816 0.71604938 0.66666667 0.69767442 0.65853659 0.69620253 0.73493976] mean value: 0.6973994937260731 key: train_precision value: [0.72334294 0.70323488 0.70936639 0.70788043 0.70861833 0.71292517 0.71526823 0.70344828 0.71732955 0.71349862] mean value: 0.7114912816510639 key: test_recall value: [0.71641791 0.82089552 0.86567164 0.8358209 0.86567164 0.79411765 0.88235294 0.79411765 0.80882353 0.89705882] mean value: 0.8280948200175592 key: train_recall value: [0.82565789 0.82236842 0.84703947 0.85690789 0.85197368 0.86326194 0.85667216 0.84019769 0.83196046 0.85337727] mean value: 0.844941689066158 key: test_accuracy value: [0.71111111 0.78518519 0.74074074 0.68888889 0.76296296 0.6962963 0.74814815 0.68888889 0.72592593 0.78518519] mean value: 0.7333333333333334 key: train_accuracy value: [0.75473251 0.73744856 0.74979424 0.75144033 0.75061728 0.75802469 0.75802469 0.74320988 0.75226337 0.75555556] mean value: 0.7511111111111111 key: test_roc_auc value: [0.71115013 0.78544776 0.74165935 0.68996927 0.76371817 0.69556629 0.74714662 0.6881036 0.72530729 0.78435031] mean value: 0.7332418788410887 key: train_roc_auc value: [0.75467409 0.73737861 0.74971414 0.75135345 0.75053379 0.75811124 0.75810582 0.74328964 0.75232891 0.755636 ] mean value: 0.7511125682823202 key: test_jcc value: [0.55172414 0.6547619 0.62365591 0.57142857 0.64444444 0.56842105 0.63829787 0.5625 0.59782609 0.67777778] mean value: 0.6090837762250755 key: train_jcc value: [0.6275 0.61050061 0.62881563 0.63304982 0.63093788 0.6405868 0.63882064 0.62043796 0.62655087 0.63558282] mean value: 0.6292783020352863 MCC on Blind test: 0.02 MCC on Training: 0.48 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.03858924 0.04097247 0.02828288 0.03735542 0.03713131 0.02666354 0.04179192 0.05626822 0.0451386 0.03369689] mean value: 0.03858904838562012 key: score_time value: [0.01252699 0.01262188 0.01268148 0.01251578 0.01253605 0.0128808 0.01299381 0.01259089 0.01311111 0.01298904] mean value: 0.012744784355163574 key: test_mcc value: [0.38264781 0.6260178 0.3890009 0.45276618 0.26528881 0.49565172 0.47488427 0.52064899 0.51937672 0.49185539] mean value: 0.4618138587697026 key: train_mcc value: [0.46921452 0.66339221 0.5480856 0.68905194 0.27794734 0.43856184 0.62213103 0.61260591 0.58257751 0.57079629] mean value: 0.5474364177561885 key: test_fscore value: [0.52083333 0.82352941 0.60550459 0.71317829 0.69430052 0.61386139 0.70967742 0.70689655 0.78313253 0.77192982] mean value: 0.6942843856861837 key: train_fscore value: [0.5717566 0.84075472 0.70210632 0.84289277 0.70046083 0.57078652 0.78584392 0.75628627 0.80642907 0.80027082] mean value: 0.7377587828850106 key: test_precision value: [0.86206897 0.73255814 0.78571429 0.74193548 0.53174603 0.93939394 0.78571429 0.85416667 0.66326531 0.6407767 ] mean value: 0.7537339803309877 key: train_precision value: [0.94676806 0.77684798 0.89974293 0.85210084 0.53900709 0.8975265 0.87474747 0.91569087 0.70024272 0.67931034] mean value: 0.8081984807946261 key: test_recall value: [0.37313433 0.94029851 0.49253731 0.68656716 1. 0.45588235 0.64705882 0.60294118 0.95588235 0.97058824] mean value: 0.7124890254609306 key: train_recall value: [0.40953947 0.91611842 0.57565789 0.83388158 1. 0.4184514 0.71334432 0.64415157 0.95057661 0.97364086] mean value: 0.7435362113066851 key: test_accuracy value: [0.65925926 0.8 0.68148148 0.72592593 0.56296296 0.71111111 0.73333333 0.74814815 0.73333333 0.71111111] mean value: 0.7066666666666667 key: train_accuracy value: [0.69300412 0.82633745 0.75555556 0.84444444 0.57201646 0.68559671 0.80576132 0.79259259 0.77201646 0.75720165] mean value: 0.7504526748971194 key: test_roc_auc value: [0.6571554 0.80103161 0.68009219 0.72563652 0.56617647 0.7130158 0.73397717 0.74923178 0.73167252 0.70917471] mean value: 0.7067164179104477 key: train_roc_auc value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") [0.69323761 0.82626349 0.75570374 0.84445315 0.57166392 0.68537702 0.80568532 0.79247052 0.7721633 0.75737964] mean value: 0.7504397706581115 key: test_jcc value: [0.35211268 0.7 0.43421053 0.55421687 0.53174603 0.44285714 0.55 0.54666667 0.64356436 0.62857143] mean value: 0.5383945696118919 key: train_jcc value: [0.40032154 0.72526042 0.54095827 0.72844828 0.53900709 0.39937107 0.64723468 0.60808709 0.67564403 0.66704289] mean value: 0.5931375354127371 MCC on Blind test: 0.49 MCC on Training: 0.46 Running classifier: 17 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.04250026 0.04533911 0.04489636 0.04625559 0.04582214 0.04467893 0.04471469 0.08213711 0.07727766 0.07820034] mean value: 0.05518221855163574 key: score_time value: [0.0134716 0.01429892 0.01341319 0.01339483 0.01337099 0.01330662 0.01328373 0.01331687 0.01687741 0.01603007] mean value: 0.014076423645019532 key: test_mcc value: [0.53156792 0.58724181 0.57105616 0.56459864 0.53057761 0.46844778 0.59600661 0.53849668 0.56153061 0.53849668] mean value: 0.5488020509170701 key: train_mcc value: [0.60629816 0.5553063 0.63170937 0.52603767 0.52731381 0.52772017 0.62702642 0.52517247 0.51624188 0.54805264] mean value: 0.5590878906477401 key: test_fscore value: [0.78313253 0.80239521 0.8 0.79289941 0.77906977 0.75977654 0.80952381 0.78612717 0.79532164 0.78612717] mean value: 0.789437323395088 key: train_fscore value: [0.81287726 0.79115159 0.82360923 0.77948718 0.77998717 0.77970456 0.82145282 0.7787043 0.7752235 0.78780013] mean value: 0.7929997739145759 key: test_precision value: [0.65656566 0.67 0.68817204 0.65686275 0.63809524 0.61261261 0.68 0.64761905 0.66019417 0.64761905] mean value: 0.6557740565377677 key: train_precision value: [0.68629672 0.65446717 0.70092379 0.63865546 0.63932702 0.63894737 0.69861432 0.63760504 0.63295099 0.64989293] mean value: 0.657768081201815 key: test_recall value: [0.97014925 1. 0.95522388 1. 1. 1. 1. 1. 1. 1. ] mean value: 0.9925373134328359 key: train_recall value: [0.99671053 1. 0.99835526 1. 1. 1. 0.99670511 1. 1. 1. ] mean value: 0.9991770896557703 key: test_accuracy value: [0.73333333 0.75555556 0.76296296 0.74074074 0.71851852 0.68148148 0.76296296 0.72592593 0.74074074 0.72592593] mean value: 0.7348148148148148 key: train_accuracy value: [0.77037037 0.73580247 0.78600823 0.71687243 0.71769547 0.71769547 0.78353909 0.71604938 0.71028807 0.7308642 ] mean value: 0.7385185185185185 key: test_roc_auc value: [0.73507463 0.75735294 0.76437665 0.74264706 0.72058824 0.67910448 0.76119403 0.7238806 0.73880597 0.7238806 ] mean value: 0.7346905179982441 key: train_roc_auc value: [0.77018393 0.73558484 0.78583332 0.71663921 0.71746293 0.71792763 0.7837144 0.71628289 0.71052632 0.73108553] mean value: 0.7385240993236798 key: test_jcc value: [0.64356436 0.67 0.66666667 0.65686275 0.63809524 0.61261261 0.68 0.64761905 0.66019417 0.64761905] mean value: 0.6523233888903577 key: train_jcc value: [0.68474576 0.65446717 0.70011534 0.63865546 0.63932702 0.63894737 0.69700461 0.63760504 0.63295099 0.64989293] mean value: 0.6573711701301338 MCC on Blind test: 0.13 MCC on Training: 0.55 Running classifier: 18 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42))]) key: fit_time value: [1.06113863 1.06064796 1.12076998 1.05886865 1.04759073 1.06484556 1.07150674 1.05287385 1.07885051 1.08677506] mean value: 1.0703867673873901 key: score_time value: [0.20774531 0.18971467 0.1903801 0.1879096 0.19546628 0.19648957 0.20337677 0.18130398 0.17982411 0.19232416] mean value: 0.1924534559249878 key: test_mcc value: [0.85327967 0.86756004 0.88188806 0.88490475 0.85532288 0.88188806 0.80951774 0.91274805 0.89637763 0.94116358] mean value: 0.8784650441448667 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.92307692 0.93430657 0.94117647 0.9375 0.92857143 0.94029851 0.90780142 0.95454545 0.94890511 0.97014925] mean value: 0.9386331135247904 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.95238095 0.91428571 0.92753623 0.98360656 0.89041096 0.95454545 0.87671233 0.984375 0.94202899 0.98484848] mean value: 0.9410730668500191 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.89552239 0.95522388 0.95522388 0.89552239 0.97014925 0.92647059 0.94117647 0.92647059 0.95588235 0.95588235] mean value: 0.9377524143985951 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.92592593 0.93333333 0.94074074 0.94074074 0.92592593 0.94074074 0.9037037 0.95555556 0.94814815 0.97037037] mean value: 0.9385185185185184 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.92570237 0.93349429 0.94084723 0.94040825 0.9262511 0.94084723 0.90342406 0.95577261 0.94809043 0.97047849] mean value: 0.9385316066725199 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.85714286 0.87671233 0.88888889 0.88235294 0.86666667 0.88732394 0.83116883 0.91304348 0.90277778 0.94202899] mean value: 0.8848106699018704 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.49 MCC on Training: 0.88 Running classifier: 19 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea...age_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42))]) key: fit_time value: [1.40944338 1.42811441 1.44919086 1.4061389 1.45089555 1.44811249 1.48700547 1.4986558 1.45409441 1.42908716] mean value: 1.446073842048645 key: score_time value: [0.2803731 0.20234346 0.24786997 0.27668142 0.25229764 0.27478361 0.26462674 0.33367133 0.23624706 0.27285314] mean value: 0.264174747467041 key: test_mcc value: [0.81163213 0.85225029 0.79388665 0.82536717 0.82261253 0.8667691 0.80813257 0.86756004 0.85218556 0.89721083] mean value: 0.8397606870064699 key: train_mcc value: [0.97039155 0.96380707 0.97695472 0.96051484 0.96875728 0.96380668 0.97037553 0.96872553 0.97202818 0.96546454] mean value: 0.9680825914407516 key: test_fscore value: [0.8976378 0.92647059 0.89230769 0.90625 0.91176471 0.93333333 0.90647482 0.93233083 0.92753623 0.94736842] mean value: 0.9181474415182507 key: train_fscore value: [0.98514851 0.98184818 0.98848684 0.98019802 0.98431049 0.98181818 0.98514851 0.98433636 0.98596201 0.98263027] mean value: 0.9839887389405938 key: test_precision value: [0.95 0.91304348 0.92063492 0.95081967 0.89855072 0.94029851 0.88732394 0.95384615 0.91428571 0.96923077] mean value: 0.9298033884151915 key: train_precision value: [0.9884106 0.98509934 0.98848684 0.98344371 0.98839138 0.98507463 0.98677686 0.98514851 0.9884106 0.98671096] mean value: 0.9865953421643375 key: test_recall value: [0.85074627 0.94029851 0.86567164 0.86567164 0.92537313 0.92647059 0.92647059 0.91176471 0.94117647 0.92647059] mean value: 0.9080114135206321 key: train_recall value: [0.98190789 0.97861842 0.98848684 0.97697368 0.98026316 0.9785832 0.98352554 0.98352554 0.98352554 0.9785832 ] mean value: 0.9813992998352553 key: test_accuracy value: [0.9037037 0.92592593 0.8962963 0.91111111 0.91111111 0.93333333 0.9037037 0.93333333 0.92592593 0.94814815] mean value: 0.9192592592592591 key: train_accuracy value: [0.98518519 0.981893 0.98847737 0.98024691 0.98436214 0.981893 0.98518519 0.98436214 0.98600823 0.98271605] mean value: 0.9840329218106996 key: test_roc_auc value: [0.90331431 0.92603161 0.89607112 0.910777 0.91121598 0.93338455 0.9035338 0.93349429 0.92581212 0.94830992] mean value: 0.919194468832309 key: train_roc_auc value: [0.98518788 0.9818957 0.98847736 0.98024961 0.98436552 0.98189028 0.98518382 0.98436145 0.98600619 0.98271265] mean value: 0.9840330464753315 key: test_jcc value: [0.81428571 0.8630137 0.80555556 0.82857143 0.83783784 0.875 0.82894737 0.87323944 0.86486486 0.9 ] mean value: 0.849131590478631 key: train_jcc value: [0.97073171 0.9643436 0.97723577 0.96116505 0.96910569 0.96428571 0.97073171 0.96915584 0.9723127 0.96585366] mean value: 0.9684921445208783 MCC on Blind test: 0.54 MCC on Training: 0.84 Running classifier: 20 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.04902506 0.04295635 0.04151678 0.05026507 0.04508829 0.04779935 0.04201984 0.03741431 0.051162 0.04570961] mean value: 0.04529566764831543 key: score_time value: [0.01914334 0.01925945 0.01977158 0.02137113 0.01947403 0.01948452 0.02460837 0.02351975 0.01950264 0.019701 ] mean value: 0.020583581924438477 key: test_mcc value: [0.54109937 0.6769471 0.55587268 0.54140139 0.61949064 0.76419015 0.65123725 0.67442373 0.64111861 0.74879186] mean value: 0.6414572772166116 key: train_mcc value: [0.73184993 0.72411039 0.72910546 0.72286546 0.73687525 0.72233703 0.71923928 0.72572674 0.72203629 0.71299947] mean value: 0.7247145297307509 key: test_fscore value: [0.76335878 0.84285714 0.77941176 0.77372263 0.81690141 0.88571429 0.83561644 0.8358209 0.83221477 0.87769784] mean value: 0.8243315948797039 key: train_fscore value: [0.86737185 0.8647343 0.86725664 0.86469175 0.87012987 0.86337914 0.8622079 0.86521388 0.86248983 0.85966319] mean value: 0.864713835014563 key: test_precision value: [0.78125 0.80821918 0.76811594 0.75714286 0.77333333 0.86111111 0.78205128 0.84848485 0.7654321 0.85915493] mean value: 0.8004295580577505 key: train_precision value: [0.85829308 0.84700315 0.8488189 0.8424337 0.85897436 0.84761905 0.84384858 0.84810127 0.85209003 0.8375 ] mean value: 0.8484682110256374 key: test_recall value: [0.74626866 0.88059701 0.79104478 0.79104478 0.86567164 0.91176471 0.89705882 0.82352941 0.91176471 0.89705882] mean value: 0.8515803336259877 key: train_recall value: [0.87664474 0.88322368 0.88651316 0.88815789 0.88157895 0.87973641 0.88138386 0.8830313 0.87314662 0.8830313 ] mean value: 0.8816447910344231 key: test_accuracy value: [0.77037037 0.83703704 0.77777778 0.77037037 0.80740741 0.88148148 0.82222222 0.83703704 0.81481481 0.87407407] mean value: 0.8192592592592594 key: train_accuracy value: [0.86584362 0.8617284 0.86419753 0.86090535 0.86831276 0.86090535 0.85925926 0.86255144 0.86090535 0.85596708] mean value: 0.8620576131687242 key: test_roc_auc value: [0.77019315 0.83735733 0.77787533 0.77052239 0.80783582 0.88125549 0.82166374 0.83713784 0.81409131 0.87390255] mean value: 0.8191834942932396 key: train_roc_auc value: [0.86583472 0.86171069 0.86417915 0.8608829 0.86830183 0.86092084 0.85927745 0.86256828 0.86091542 0.85598933] mean value: 0.8620580616491805 key: test_jcc value: [0.61728395 0.72839506 0.63855422 0.63095238 0.69047619 0.79487179 0.71764706 0.71794872 0.71264368 0.78205128] mean value: 0.7030824332497965 key: train_jcc value: [0.7658046 0.76170213 0.765625 0.76163611 0.77011494 0.75960171 0.75779037 0.76244666 0.75822604 0.75386779] mean value: 0.7616815336546742 MCC on Blind test: 0.46 MCC on Training: 0.64 Running classifier: 21 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=3))]) key: fit_time value: [0.07941699 0.1313262 0.17589927 0.10673594 0.09216619 0.13913774 0.13657451 0.14386058 0.16602349 0.14380741] mean value: 0.1314948320388794 key: score_time value: [0.01253295 0.01941752 0.02739215 0.01243997 0.03259254 0.01924229 0.0193584 0.01927352 0.01930737 0.01942945] mean value: 0.020098614692687988 key: test_mcc value: [0.69060055 0.70505556 0.67405619 0.6157076 0.67912076 0.77860768 0.65978079 0.69284095 0.63274178 0.73369925] mean value: 0.6862211111907157 key: train_mcc value: [0.78622094 0.76478816 0.78454544 0.78454544 0.7698908 0.76813056 0.75490089 0.75652209 0.7648373 0.74837103] mean value: 0.7682752642023101 key: test_fscore value: [0.8372093 0.85507246 0.8358209 0.8 0.84507042 0.88721805 0.83453237 0.8372093 0.82517483 0.86567164] mean value: 0.8422979272656249 key: train_fscore value: [0.89184692 0.88113051 0.89110557 0.89110557 0.88313856 0.882402 0.87593672 0.87687188 0.88073394 0.87239366] mean value: 0.882666533956292 key: test_precision value: [0.87096774 0.83098592 0.8358209 0.82539683 0.8 0.90769231 0.81690141 0.8852459 0.78666667 0.87878788] mean value: 0.8438465541584558 key: train_precision value: [0.9023569 0.8907563 0.90084034 0.90084034 0.89661017 0.89358108 0.88552189 0.88571429 0.89189189 0.88344595] mean value: 0.8931559136793433 key: test_recall value: [0.80597015 0.88059701 0.8358209 0.7761194 0.89552239 0.86764706 0.85294118 0.79411765 0.86764706 0.85294118] mean value: 0.8429323968393329 key: train_recall value: [0.88157895 0.87171053 0.88157895 0.88157895 0.87006579 0.87149918 0.86655684 0.86820428 0.86985173 0.8616145 ] mean value: 0.8724239681782711 key: test_accuracy value: [0.84444444 0.85185185 0.83703704 0.80740741 0.83703704 0.88888889 0.82962963 0.84444444 0.81481481 0.86666667] mean value: 0.8422222222222222 key: train_accuracy value: [0.89300412 0.88230453 0.89218107 0.89218107 0.88477366 0.88395062 0.87736626 0.8781893 0.88230453 0.87407407] mean value: 0.8840329218106996 key: test_roc_auc value: [0.84416155 0.85206321 0.83702809 0.80717735 0.83746708 0.88904741 0.82945566 0.84482002 0.81442054 0.8667691 ] mean value: 0.8422410008779633 key: train_roc_auc value: [0.89301353 0.88231325 0.8921898 0.8921898 0.88478578 0.88394038 0.87735737 0.87818109 0.88229429 0.87406383] mean value: 0.8840329109945374 key: test_jcc value: [0.72 0.74683544 0.71794872 0.66666667 0.73170732 0.7972973 0.71604938 0.72 0.70238095 0.76315789] mean value: 0.7282043671857672 key: train_jcc value: [0.8048048 0.78751857 0.8035982 0.8035982 0.79073244 0.78955224 0.77925926 0.78074074 0.78688525 0.77366864] mean value: 0.7900358340388379 MCC on Blind test: 0.41 MCC on Training: 0.69 Running classifier: 22 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.0943563 0.07776356 0.09372449 0.09208918 0.09496999 0.09315586 0.09250474 0.09777093 0.08878064 0.09370828] mean value: 0.09188239574432373 key: score_time value: [0.02662134 0.02864289 0.03023481 0.03069997 0.02824235 0.02754569 0.02711391 0.02953362 0.02910876 0.02991271] mean value: 0.028765606880187988 key: test_mcc value: [0.55744809 0.74879186 0.63011044 0.67510062 0.64571485 0.67652324 0.62124167 0.71930611 0.7191215 0.82373698] mean value: 0.6817095365113724 key: train_mcc value: [0.7630694 0.75993943 0.76970109 0.76803045 0.76855995 0.75172884 0.76490303 0.77992876 0.78342613 0.75814522] mean value: 0.7667432306117619 key: test_fscore value: [0.765625 0.87022901 0.80916031 0.83076923 0.82608696 0.84507042 0.82191781 0.85714286 0.86330935 0.90909091] mean value: 0.839840184977421 key: train_fscore value: [0.88059701 0.87833333 0.88372093 0.88298755 0.88161209 0.87385129 0.88053467 0.88758389 0.88888889 0.87780549] mean value: 0.8815915153741495 key: test_precision value: [0.80327869 0.890625 0.828125 0.85714286 0.8028169 0.81081081 0.76923077 0.87692308 0.84507042 0.9375 ] mean value: 0.8421523526575765 key: train_precision value: [0.88795987 0.8902027 0.89261745 0.89112228 0.90051458 0.88644068 0.89322034 0.9042735 0.90877797 0.88590604] mean value: 0.8941035406914729 key: test_recall value: [0.73134328 0.85074627 0.79104478 0.80597015 0.85074627 0.88235294 0.88235294 0.83823529 0.88235294 0.88235294] mean value: 0.8397497805092187 key: train_recall value: [0.87335526 0.86677632 0.875 0.875 0.86348684 0.8616145 0.86820428 0.87149918 0.86985173 0.86985173] mean value: 0.8694639837856585 key: test_accuracy value: [0.77777778 0.87407407 0.81481481 0.83703704 0.82222222 0.83703704 0.80740741 0.85925926 0.85925926 0.91111111] mean value: 0.8400000000000001 key: train_accuracy value: [0.88148148 0.87983539 0.88477366 0.88395062 0.88395062 0.87572016 0.88230453 0.88971193 0.89135802 0.87901235] mean value: 0.8832098765432098 key: test_roc_auc value: [0.77743635 0.87390255 0.81464004 0.8368086 0.82243196 0.83669886 0.80684811 0.85941615 0.85908692 0.91132572] mean value: 0.8398595258999123 key: train_roc_auc value: [0.88148818 0.87984615 0.88478171 0.88395799 0.88396747 0.87570856 0.88229293 0.88969696 0.89134034 0.87900481] mean value: 0.8832085103615711 key: test_jcc value: [0.62025316 0.77027027 0.67948718 0.71052632 0.7037037 0.73170732 0.69767442 0.75 0.75949367 0.83333333] mean value: 0.7256449373704821 key: train_jcc value: [0.78666667 0.78306092 0.79166667 0.79049034 0.78828829 0.77596439 0.78656716 0.79788839 0.8 0.78222222] mean value: 0.7882815048839509 MCC on Blind test: 0.45 MCC on Training: 0.68 Running classifier: 23 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=12, random_state=42) Running model pipeline: /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:463: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_CV['source_data'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:490: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_BT['source_data'] = 'BT' Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.03521037 0.06527209 0.0533421 0.05371308 0.05366182 0.06581569 0.0562675 0.0642736 0.06109643 0.05249143] mean value: 0.05611441135406494 key: score_time value: [0.01054716 0.01207495 0.01240158 0.0123558 0.01244378 0.01183248 0.01775837 0.01251292 0.01255965 0.01246691] mean value: 0.01269536018371582 key: test_mcc value: [0.57257202 0.35156598 0.39693383 0.38734257 0.54749791 0.66175381 0.62971905 0.66087126 0.64295076 0.48027581] mean value: 0.5331483000739947 key: train_mcc value: [0.63123312 0.32054279 0.48132412 0.54047387 0.67018908 0.66455661 0.71437415 0.6867571 0.71367966 0.52853293] mean value: 0.595166342632403 key: test_fscore value: [0.8 0.4 0.73446328 0.54545455 0.74796748 0.84076433 0.81481481 0.83687943 0.83333333 0.76404494] mean value: 0.7317722157768177 key: train_fscore value: [0.82724014 0.35904255 0.76359039 0.66737288 0.80223881 0.84118927 0.84432718 0.85112782 0.86318972 0.78131084] mean value: 0.7600629597162136 key: test_precision value: [0.72289157 0.94444444 0.59090909 0.84375 0.82142857 0.74157303 0.82089552 0.80821918 0.73863636 0.61818182] mean value: 0.7650929589043465 key: train_precision value: [0.73316391 0.9375 0.6201232 0.9375 0.92672414 0.75129534 0.90566038 0.78284924 0.79748603 0.64453961] mean value: 0.80368418563198 key: test_recall value: [0.89552239 0.25373134 0.97014925 0.40298507 0.68656716 0.97058824 0.80882353 0.86764706 0.95588235 1. ] mean value: 0.7811896400351185 key: train_recall value: [0.94901316 0.22203947 0.99342105 0.51809211 0.70723684 0.95551895 0.7907743 0.9324547 0.94069193 0.99176277] mean value: 0.800100526749328 key: test_accuracy value: [0.77777778 0.62222222 0.65185185 0.66666667 0.77037037 0.81481481 0.81481481 0.82962963 0.80740741 0.68888889] mean value: 0.7444444444444445 key: train_accuracy value: [0.80164609 0.60329218 0.69218107 0.74156379 0.8255144 0.81975309 0.85432099 0.83703704 0.85102881 0.72263374] mean value: 0.7748971193415638 key: test_roc_auc value: [0.77864355 0.61951273 0.65419227 0.66472783 0.76975417 0.81365233 0.81485953 0.82934592 0.80629939 0.68656716] mean value: 0.7437554872695347 key: train_roc_auc value: [0.8015247 0.60360623 0.69193293 0.74174786 0.82561183 0.81986474 0.85426873 0.83711551 0.85110254 0.72285507] mean value: 0.7749630137431718 key: test_jcc value: [0.66666667 0.25 0.58035714 0.375 0.5974026 0.72527473 0.6875 0.7195122 0.71428571 0.61818182] mean value: 0.5934180859790616 key: train_jcc value: [0.70537897 0.21880065 0.61758691 0.50079491 0.66978193 0.72590738 0.73059361 0.7408377 0.75930851 0.64110756] mean value: 0.6310098137237541 MCC on Blind test: 0.28 MCC on Training: 0.53 Running classifier: 24 Model_name: XGBoost Model func: XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.28155398 0.25307822 0.2714026 0.26070619 0.4271667 0.27735615 0.24479556 0.25589895 0.25376844 0.24184489] mean value: 0.2767571687698364 key: score_time value: [0.01258469 0.01189375 0.01270652 0.01185083 0.01169467 0.01177382 0.01284027 0.01266885 0.01178193 0.01179194] mean value: 0.012158727645874024 key: test_mcc value: [0.89637763 0.92603161 0.91152582 0.91267269 0.85340734 0.92686161 0.85327967 0.9565124 0.88147498 0.94116358] mean value: 0.9059307313247021 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.94736842 0.96296296 0.95588235 0.95384615 0.92753623 0.96240602 0.92857143 0.97744361 0.94117647 0.97014925] mean value: 0.9527342899638139 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.95454545 0.95588235 0.94202899 0.98412698 0.90140845 0.98461538 0.90277778 1. 0.94117647 0.98484848] mean value: 0.9551410345654968 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.94029851 0.97014925 0.97014925 0.92537313 0.95522388 0.94117647 0.95588235 0.95588235 0.94117647 0.95588235] mean value: 0.9511194029850746 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.94814815 0.96296296 0.95555556 0.95555556 0.92592593 0.96296296 0.92592593 0.97777778 0.94074074 0.97037037] mean value: 0.9525925925925925 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.94809043 0.9630158 0.95566286 0.95533363 0.92614135 0.96312555 0.92570237 0.97794118 0.94073749 0.97047849] mean value: 0.9526229148375769 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.9 0.92857143 0.91549296 0.91176471 0.86486486 0.92753623 0.86666667 0.95588235 0.88888889 0.94202899] mean value: 0.9101697082953162 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.53 MCC on Training: 0.91 Extracting tts_split_name: sl Total cols in each df: CV df: 8 metaDF: 17 Adding column: Model_name Total cols in bts df: BT_df: 8 First proceeding to rowbind CV and BT dfs: Final output should have: 25 columns Combinig 2 using pd.concat by row ~ rowbind Checking Dims of df to combine: Dim of CV: (24, 8) Dim of BT: [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... 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Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... 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Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.4s remaining: 0.8s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.5s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.4s remaining: 0.8s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.5s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... 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Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... ÿÿÿÿAPQzâV\W¦¡[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.4s remaining: 0.9s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.5s remaining: 0.2s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.5s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... amples”Œweighted_n_node_samples”t”}”(h hŒi8”‰ˆ‡”R”(KŒ<”NNNJÿÿÿÿJÿÿÿÿKt”bK†”h hK†”h hK†”hhŒf8”‰ˆ‡”R”(KhNNNJÿÿÿÿBuilding estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.4s remaining: 0.8s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.4s remaining: 0.8s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.5s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.4s remaining: 0.8s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.4s remaining: 0.8s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.5s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.4s remaining: 0.9s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.5s remaining: 0.2s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.5s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.4s remaining: 0.8s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.5s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.4s remaining: 0.8s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.5s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished (24, 8) 8 Number of Common columns: 8 These are: ['source_data', 'Precision', 'JCC', 'MCC', 'Recall', 'Accuracy', 'F1', 'ROC_AUC'] Concatenating dfs with different resampling methods [WF]: Split type: sl No. of dfs combining: 2 PASS: 2 dfs successfully combined nrows in combined_df_wf: 48 ncols in combined_df_wf: 8 PASS: proceeding to merge metadata with CV and BT dfs Adding column: Model_name ========================================================= SUCCESS: Ran multiple classifiers ======================================================= ============================================================== Running several classification models (n): 24 List of models: ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('Gaussian NB', GaussianNB()) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('K-Nearest Neighbors', KNeighborsClassifier()) ('LDA', LinearDiscriminantAnalysis()) ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(cv=3, random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Multinomial', MultinomialNB()) ('Naive Bayes', BernoulliNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=12, random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Random Forest', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42)) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=3)) ('SVC', SVC(random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=12, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ================================================================ Running classifier: 1 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.33754492 0.31181264 0.34132743 0.34558272 0.35178423 0.34610701 0.33033204 0.3379457 0.32800508 0.33352923] mean value: 0.33639709949493407 key: score_time value: [0.01752472 0.01624823 0.01748157 0.01739478 0.01853347 0.01881647 0.01716447 0.01696944 0.01794004 0.01724887] mean value: 0.017532205581665038 key: test_mcc value: [0.70406149 0.61197315 0.66000454 0.70393163 0.68537472 0.6151482 0.60000001 0.68945115 0.73449364 0.70393163] mean value: 0.6708370146971409 key: train_mcc value: [0.81759982 0.76802354 0.76355122 0.76813056 0.82944847 0.78299221 0.77623974 0.76338301 0.80662572 0.75679525] mean value: 0.7832789533597987 key: test_fscore value: [0.85294118 0.81632653 0.83211679 0.84848485 0.84931507 0.80597015 0.80291971 0.84892086 0.87142857 0.85507246] mean value: 0.8383496168170967 key: train_fscore value: [0.90997567 0.88508557 0.88387097 0.88545898 0.91612903 0.89268293 0.88888889 0.88330632 0.90499195 0.88006483] mean value: 0.8930455135031503 key: test_precision value: [0.84057971 0.75 0.81428571 0.86153846 0.78481013 0.81818182 0.79710145 0.83098592 0.84722222 0.84285714] mean value: 0.8187562560580884 key: train_precision value: [0.8976 0.87722132 0.86708861 0.87479936 0.89873418 0.8812199 0.88168558 0.8692185 0.88503937 0.86602871] mean value: 0.879863552553948 key: test_recall value: [0.86567164 0.89552239 0.85074627 0.8358209 0.92537313 0.79411765 0.80882353 0.86764706 0.89705882 0.86764706] mean value: 0.8608428446005268 key: train_recall value: [0.92269737 0.89309211 0.90131579 0.89638158 0.93421053 0.90444811 0.89621087 0.89785832 0.92586491 0.89456343] mean value: 0.906664300268794 key: test_accuracy value: [0.85185185 0.8 0.82962963 0.85185185 0.83703704 0.80740741 0.8 0.84444444 0.86666667 0.85185185] mean value: 0.834074074074074 key: train_accuracy value: [0.90864198 0.88395062 0.88148148 0.88395062 0.91440329 0.89135802 0.88806584 0.88148148 0.90288066 0.8781893 ] mean value: 0.891440329218107 key: test_roc_auc value: [0.85195347 0.80070237 0.8297849 0.85173398 0.83768657 0.80750658 0.79993415 0.84427129 0.86643986 0.85173398] mean value: 0.8341747146619841 key: train_roc_auc value: [0.9086304 0.88394309 0.88146514 0.88394038 0.91438698 0.89136879 0.88807254 0.88149495 0.90289956 0.87820277] mean value: 0.8914404589005462 key: test_jcc value: [0.74358974 0.68965517 0.7125 0.73684211 0.73809524 0.675 0.67073171 0.7375 0.7721519 0.74683544] mean value: 0.7222901308451156 key: train_jcc value: [0.83482143 0.79385965 0.79190751 0.79446064 0.8452381 0.8061674 0.8 0.79100145 0.82647059 0.78581766] mean value: 0.8069744424849411 MCC on Blind test: 0.33 MCC on Training: 0.67 Running classifier: 2 Model_name: Bagging Classifier Model func: BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3))]) key: fit_time value: [0.67845058 0.71161842 0.73637128 0.66431737 0.68866873 0.63738441 0.70144153 0.69565654 0.64732361 0.72979403] [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... 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Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 3.7s remaining: 7.4s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 3.7s remaining: 7.4s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 3.7s remaining: 7.5s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 3.8s remaining: 7.5s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 3.8s remaining: 7.5s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 3.8s remaining: 7.6s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 3.8s remaining: 7.7s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 3.8s remaining: 7.7s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 3.9s remaining: 1.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 3.9s remaining: 1.3s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 3.9s remaining: 7.8s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 3.9s remaining: 7.8s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 3.9s remaining: 1.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 3.9s remaining: 1.3s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 3.9s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 3.9s remaining: 1.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 3.9s remaining: 1.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 4.0s remaining: 1.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 4.0s remaining: 1.3s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 4.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 4.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 4.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 4.0s remaining: 1.3s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 4.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 4.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 4.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 4.0s remaining: 1.3s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 4.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 4.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 4.1s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.4s remaining: 0.9s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.5s remaining: 0.2s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.5s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished mean value: 0.6891026496887207 key: score_time value: [0.06477809 0.08538556 0.04720044 0.05574822 0.05450988 0.07911325 0.07770777 0.05495167 0.04999328 0.05799246] mean value: 0.06273806095123291 key: test_mcc value: [0.9565124 0.90122245 0.92851083 0.98529412 0.92851083 0.95648435 0.92681399 0.98529091 0.9423692 0.95648435] mean value: 0.9467493415496231 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.97810219 0.95035461 0.96402878 0.99259259 0.96402878 0.97841727 0.96402878 0.99270073 0.97142857 0.97841727] mean value: 0.9734099556967623 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.95714286 0.90540541 0.93055556 0.98529412 0.93055556 0.95774648 0.94366197 0.98550725 0.94444444 0.95774648] mean value: 0.9498060111705154 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 0.98529412 1. 1. 1. ] mean value: 0.9985294117647058 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.97777778 0.94814815 0.96296296 0.99259259 0.96296296 0.97777778 0.96296296 0.99259259 0.97037037 0.97777778] mean value: 0.9725925925925927 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.97794118 0.94852941 0.96323529 0.99264706 0.96323529 0.97761194 0.96279631 0.99253731 0.97014925 0.97761194] mean value: 0.9726294995610184 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.95714286 0.90540541 0.93055556 0.98529412 0.93055556 0.95774648 0.93055556 0.98550725 0.94444444 0.95774648] mean value: 0.9484953695429723 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.54 MCC on Training: 0.95 Running classifier: 3 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.08054018 0.06094313 0.07738996 0.06342554 0.06208515 0.06721163 0.07350302 0.07029629 0.06746674 0.07027125] mean value: 0.06931328773498535 key: score_time value: [0.01043868 0.01024461 0.01044321 0.01109934 0.01119614 0.01113677 0.01128721 0.01084042 0.00972748 0.01076937] mean value: 0.010718321800231934 key: test_mcc value: [0.88782616 0.82303688 0.88782616 0.84854147 0.88782616 0.86120627 0.79740633 0.88763877 0.86120627 0.91467353] mean value: 0.865718800914904 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.94366197 0.91156463 0.94366197 0.92413793 0.94366197 0.93150685 0.90066225 0.94444444 0.93150685 0.95774648] mean value: 0.933255534598123 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.89333333 0.8375 0.89333333 0.85897436 0.89333333 0.87179487 0.81927711 0.89473684 0.87179487 0.91891892] mean value: 0.875299697202202 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.94074074 0.9037037 0.94074074 0.91851852 0.94074074 0.92592593 0.88888889 0.94074074 0.92592593 0.95555556] mean value: 0.9281481481481482 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.94117647 0.90441176 0.94117647 0.91911765 0.94117647 0.92537313 0.8880597 0.94029851 0.92537313 0.95522388] mean value: 0.9281387181738368 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.89333333 0.8375 0.89333333 0.85897436 0.89333333 0.87179487 0.81927711 0.89473684 0.87179487 0.91891892] mean value: 0.875299697202202 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.23 MCC on Training: 0.87 Running classifier: 4 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.01552391 0.01562095 0.01580739 0.01528859 0.01581454 0.01605773 0.01344776 0.01370406 0.01303196 0.01352906] mean value: 0.014782595634460449 key: score_time value: [0.01053834 0.01013088 0.01029634 0.00949216 0.01056576 0.0131669 0.00982261 0.00892711 0.00924516 0.00903821] mean value: 0.010122346878051757 key: test_mcc value: [0.84854147 0.88782616 0.82303688 0.88782616 0.78567977 0.86120627 0.87435075 0.87435075 0.9010773 0.86120627] mean value: 0.8605101784602134 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.92413793 0.94366197 0.91156463 0.94366197 0.89333333 0.93150685 0.93793103 0.93793103 0.95104895 0.93150685] mean value: 0.9306284552524733 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.85897436 0.89333333 0.8375 0.89333333 0.80722892 0.87179487 0.88311688 0.88311688 0.90666667 0.87179487] mean value: 0.8706860117793852 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.91851852 0.94074074 0.9037037 0.94074074 0.88148148 0.92592593 0.93333333 0.93333333 0.94814815 0.92592593] mean value: 0.9251851851851851 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.91911765 0.94117647 0.90441176 0.94117647 0.88235294 0.92537313 0.93283582 0.93283582 0.94776119 0.92537313] mean value: 0.925241439859526 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.85897436 0.89333333 0.8375 0.89333333 0.80722892 0.87179487 0.88311688 0.88311688 0.90666667 0.87179487] mean value: 0.8706860117793852 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.16 MCC on Training: 0.86 Running classifier: 5 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.21269727 0.21755862 0.19843602 0.21276307 0.2110815 0.21698999 0.20903873 0.21688461 0.21972013 0.21570802] mean value: 0.213087797164917 key: score_time value: [0.02122593 0.0212574 0.0197227 0.02120686 0.02221203 0.02094603 0.02064085 0.02142143 0.02180767 0.02080894] mean value: 0.021124982833862306 key: test_mcc value: [0.9424184 1. 0.98529412 0.98529412 0.97080134 0.98529091 0.97036874 1. 1. 1. ] mean value: 0.983946763512596 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.97101449 1. 0.99259259 0.99259259 0.98529412 0.99270073 0.98529412 1. 1. 1. ] mean value: 0.9919488643159934 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.94366197 1. 0.98529412 0.98529412 0.97101449 0.98550725 0.98529412 1. 1. 1. ] mean value: 0.9856066063902598 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 0.98529412 1. 1. 1. ] mean value: 0.9985294117647058 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.97037037 1. 0.99259259 0.99259259 0.98518519 0.99259259 0.98518519 1. 1. 1. ] mean value: 0.9918518518518518 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.97058824 1. 0.99264706 0.99264706 0.98529412 0.99253731 0.98518437 1. 1. 1. ] mean value: 0.9918898156277436 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.94366197 1. 0.98529412 0.98529412 0.97101449 0.98550725 0.97101449 1. 1. 1. ] mean value: 0.9841786439009163 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.07 MCC on Training: 0.98 Running classifier: 6 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [1.43616128 1.40891719 1.42119908 1.42642403 1.45573807 1.43027997 1.44979119 1.44372058 1.46938133 1.413692 ] mean value: 1.4355304718017579 key: score_time value: [0.00990295 0.00998855 0.00997758 0.00962234 0.01050234 0.01045108 0.01086593 0.00995922 0.00973105 0.01007295] mean value: 0.01010739803314209 key: test_mcc value: [0.83790925 0.86149266 0.91152582 0.91152582 0.87458526 0.88183634 0.87126224 0.92601539 0.87126224 0.95648435] mean value: 0.8903899369788609 key: train_mcc value: [0.96411966 0.97239662 0.97385212 0.96733445 0.97239662 0.96404269 0.96082795 0.95942224 0.97070771 0.96741445] mean value: 0.9672514517835837 key: test_fscore value: [0.91970803 0.93055556 0.95588235 0.95588235 0.93706294 0.94202899 0.93706294 0.96350365 0.93706294 0.97841727] mean value: 0.9457167003153133 key: train_fscore value: [0.98214286 0.98621249 0.98697068 0.98373984 0.98621249 0.98208469 0.9804878 0.97975709 0.98536585 0.98373984] mean value: 0.9836713629813518 key: test_precision value: [0.9 0.87012987 0.94202899 0.94202899 0.88157895 0.92857143 0.89333333 0.95652174 0.89333333 0.95774648] mean value: 0.9165273101754554 key: train_precision value: [0.96955128 0.9728 0.97741935 0.97266881 0.9728 0.97101449 0.96789727 0.9633758 0.97271268 0.97110754] mean value: 0.9711347232098507 key: test_recall value: [0.94029851 1. 0.97014925 0.97014925 1. 0.95588235 0.98529412 0.97058824 0.98529412 1. ] mean value: 0.9777655838454784 key: train_recall value: [0.99506579 1. 0.99671053 0.99506579 1. 0.99341021 0.99341021 0.99670511 0.99835255 0.99670511] mean value: 0.9965425301309286 key: test_accuracy value: [0.91851852 0.92592593 0.95555556 0.95555556 0.93333333 0.94074074 0.93333333 0.96296296 0.93333333 0.97777778] mean value: 0.9437037037037037 key: train_accuracy value: [0.981893 0.98600823 0.98683128 0.98353909 0.98600823 0.981893 0.98024691 0.97942387 0.98518519 0.98353909] mean value: 0.9834567901234568 key: test_roc_auc value: [0.91867867 0.92647059 0.95566286 0.95566286 0.93382353 0.94062774 0.93294557 0.96290606 0.93294557 0.97761194] mean value: 0.9437335381913959 key: train_roc_auc value: [0.98188215 0.98599671 0.98682314 0.9835296 0.98599671 0.98190248 0.98025774 0.97943808 0.98519601 0.98354992] mean value: 0.9834572530998006 key: test_jcc value: [0.85135135 0.87012987 0.91549296 0.91549296 0.88157895 0.89041096 0.88157895 0.92957746 0.88157895 0.95774648] mean value: 0.8974938881645524 key: train_jcc value: [0.96491228 0.9728 0.97427653 0.968 0.9728 0.9648 0.96172249 0.96031746 0.97115385 0.968 ] mean value: 0.9678782602542528 MCC on Blind test: 0.43 MCC on Training: 0.89 Running classifier: 7 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.01468444 0.01464629 0.01373863 0.01412272 0.01360726 0.01424289 0.01470709 0.01405978 0.01466465 0.01513028] mean value: 0.014360404014587403 key: score_time value: [0.01054239 0.01033664 0.01049209 0.00944066 0.01033878 0.01042247 0.01032352 0.01018643 0.00993872 0.01057196] mean value: 0.010259366035461426 key: test_mcc value: [0.34843903 0.27406617 0.42303688 0.48249548 0.26292596 0.4666374 0.36556907 0.37774363 0.46884202 0.37868165] mean value: 0.3848437292164657 key: train_mcc value: [0.4320982 0.40742206 0.42070039 0.4345664 0.41153534 0.4532447 0.41569021 0.44526697 0.38933164 0.3762993 ] mean value: 0.4186155198227479 key: test_fscore value: [0.66153846 0.6259542 0.69767442 0.72868217 0.65277778 0.73529412 0.70344828 0.69117647 0.72307692 0.68181818] mean value: 0.6901440995929276 key: train_fscore value: [0.71651602 0.70540098 0.69595177 0.72567783 0.69609508 0.73592387 0.70973017 0.72217642 0.6961507 0.68284519] mean value: 0.7086468026247597 key: test_precision value: [0.68253968 0.640625 0.72580645 0.75806452 0.61038961 0.73529412 0.66233766 0.69117647 0.75806452 0.703125 ] mean value: 0.6967423027373216 key: train_precision value: [0.71592775 0.7019544 0.73056058 0.70433437 0.71929825 0.70948012 0.70454545 0.72277228 0.69218241 0.69387755] mean value: 0.7094933152946801 key: test_recall value: [0.64179104 0.6119403 0.67164179 0.70149254 0.70149254 0.73529412 0.75 0.69117647 0.69117647 0.66176471] mean value: 0.6857769973661106 key: train_recall value: [0.71710526 0.70888158 0.66447368 0.74835526 0.67434211 0.76441516 0.71499176 0.72158155 0.70016474 0.67215815] mean value: 0.7086469262117403 key: test_accuracy value: [0.67407407 0.63703704 0.71111111 0.74074074 0.62962963 0.73333333 0.68148148 0.68888889 0.73333333 0.68888889] mean value: 0.6918518518518518 key: train_accuracy value: [0.71604938 0.7037037 0.70946502 0.71687243 0.70534979 0.72592593 0.70781893 0.72263374 0.69465021 0.68806584] mean value: 0.7090534979423869 key: test_roc_auc value: [0.6738367 0.6368525 0.7108209 0.74045215 0.63015803 0.7333187 0.68097015 0.68887182 0.73364794 0.68909131] mean value: 0.6918020193151888 key: train_roc_auc value: [0.71604851 0.70369944 0.70950208 0.71684649 0.70537534 0.72595758 0.70782483 0.72263288 0.69465474 0.68805276] mean value: 0.709059465230209 key: test_jcc value: [0.49425287 0.45555556 0.53571429 0.57317073 0.48453608 0.58139535 0.54255319 0.52808989 0.56626506 0.51724138] mean value: 0.5278774396532933 key: train_jcc value: [0.55825864 0.5448799 0.5336856 0.56946183 0.53385417 0.58218319 0.55006337 0.56516129 0.5339196 0.5184244 ] mean value: 0.548989197969739 MCC on Blind test: 0.4 MCC on Training: 0.38 Running classifier: 8 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.75903535 0.74290705 0.77943873 0.73659015 0.72583723 0.96419263 0.71266079 0.82801247 0.73660326 0.7410326 ] mean value: 0.772631025314331 key: score_time value: [0.03015614 0.05257392 0.05160475 0.05374169 0.0548172 0.0515542 0.04957366 0.02907586 0.02850604 0.03834963] mean value: 0.0439953088760376 key: test_mcc value: [0.72524424 0.78002828 0.77170765 0.75237852 0.74551484 0.82358316 0.74122397 0.75005915 0.80951774 0.84144718] mean value: 0.7740704742497624 key: train_mcc value: [0.93357199 0.91365684 0.92210952 0.92468394 0.93158922 0.92659246 0.93000823 0.92308724 0.92870791 0.93530005] mean value: 0.9269307402851483 key: test_fscore value: [0.86713287 0.89208633 0.88888889 0.87943262 0.87671233 0.91428571 0.87671233 0.87943262 0.90780142 0.92307692] mean value: 0.8905562048520558 key: train_fscore value: [0.96706827 0.95736122 0.96147673 0.96272285 0.96612903 0.96362167 0.96529459 0.96188159 0.96463023 0.96784566] mean value: 0.9638031837827954 key: test_precision value: [0.81578947 0.86111111 0.83116883 0.83783784 0.81012658 0.88888889 0.82051282 0.84931507 0.87671233 0.88 ] mean value: 0.8471462942742456 key: train_precision value: [0.94505495 0.93700787 0.93887147 0.94888179 0.94778481 0.94603175 0.94620253 0.94728435 0.94191523 0.94505495] mean value: 0.9444089687098586 key: test_recall value: [0.92537313 0.92537313 0.95522388 0.92537313 0.95522388 0.94117647 0.94117647 0.91176471 0.94117647 0.97058824] mean value: 0.9392449517120282 key: train_recall value: [0.99013158 0.97861842 0.98519737 0.97697368 0.98519737 0.98187809 0.98517298 0.97693575 0.98846787 0.99176277] mean value: 0.9840335883985087 key: test_accuracy value: [0.85925926 0.88888889 0.88148148 0.87407407 0.86666667 0.91111111 0.86666667 0.87407407 0.9037037 0.91851852] mean value: 0.8844444444444445 key: train_accuracy value: [0.96625514 0.9563786 0.96049383 0.96213992 0.9654321 0.96296296 0.96460905 0.96131687 0.96378601 0.96707819] mean value: 0.963045267489712 key: test_roc_auc value: [0.85974539 0.88915716 0.88202371 0.87445127 0.86731782 0.91088674 0.86611062 0.8737928 0.90342406 0.91812994] mean value: 0.884503950834065 key: train_roc_auc value: [0.96623548 0.95636028 0.96047348 0.9621277 0.96541582 0.96297852 0.96462596 0.96132972 0.96380631 0.96709849] mean value: 0.9630451747160322 key: test_jcc value: [0.7654321 0.80519481 0.8 0.78481013 0.7804878 0.84210526 0.7804878 0.78481013 0.83116883 0.85714286] mean value: 0.8031639718350476 key: train_jcc value: [0.93623639 0.91820988 0.92581144 0.928125 0.93447738 0.92979719 0.93291732 0.9265625 0.93167702 0.9376947 ] mean value: 0.930150881621841 MCC on Blind test: 0.45 MCC on Training: 0.77 Running classifier: 9 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.0161047 0.01155567 0.01165581 0.01165462 0.01284075 0.01203537 0.01872182 0.01210213 0.01293063 0.01179266] mean value: 0.01313941478729248 key: score_time value: [0.0375123 0.01844788 0.02036691 0.01839685 0.01657891 0.01624155 0.02440143 0.02151752 0.02112198 0.02307606] mean value: 0.021766138076782227 key: test_mcc value: [0.6260178 0.74925864 0.60301599 0.65530465 0.69432232 0.63219979 0.62450368 0.65823755 0.66275724 0.65440924] mean value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( 0.6560026905637707 key: train_mcc value: [0.79475177 0.78160086 0.77848284 0.7706717 0.77206457 0.76761322 0.77182266 0.77729746 0.77518863 0.7690151 ] mean value: 0.7758508820321801 key: test_fscore value: [0.82352941 0.87581699 0.81333333 0.83561644 0.8516129 0.82894737 0.82580645 0.84 0.84210526 0.83783784] mean value: 0.8374606001173751 key: train_fscore value: [0.89932886 0.89317507 0.89203276 0.88823094 0.88888889 0.88690476 0.88888889 0.89172932 0.89038031 0.88756515] mean value: 0.890712496459412 key: test_precision value: [0.73255814 0.77906977 0.73493976 0.7721519 0.75 0.75 0.73563218 0.76829268 0.76190476 0.775 ] mean value: 0.7559549193486703 key: train_precision value: [0.82264666 0.81351351 0.81496599 0.80753701 0.80862534 0.80868385 0.8119891 0.82019364 0.8133515 0.80978261] mean value: 0.8131289205751664 key: test_recall value: [0.94029851 1. 0.91044776 0.91044776 0.98507463 0.92647059 0.94117647 0.92647059 0.94117647 0.91176471] mean value: 0.939332748024583 key: train_recall value: [0.99177632 0.99013158 0.98519737 0.98684211 0.98684211 0.98187809 0.98187809 0.97693575 0.98352554 0.98187809] mean value: 0.9846885025578775 key: test_accuracy value: [0.8 0.85925926 0.79259259 0.82222222 0.82962963 0.80740741 0.8 0.82222222 0.82222222 0.82222222] mean value: 0.8177777777777777 key: train_accuracy value: [0.88888889 0.88148148 0.88065844 0.87572016 0.87654321 0.87489712 0.87736626 0.88148148 0.87901235 0.87572016] mean value: 0.8791769547325103 key: test_roc_auc value: [0.80103161 0.86029412 0.79345917 0.82287094 0.83077261 0.80651888 0.79894644 0.82144425 0.8213345 0.82155399] mean value: 0.8178226514486392 key: train_roc_auc value: [0.88880414 0.88139198 0.88057233 0.87562863 0.87645235 0.8749851 0.8774522 0.88155998 0.87909829 0.87580747] mean value: 0.8791752471169687 key: test_jcc value: [0.7 0.77906977 0.68539326 0.71764706 0.74157303 0.70786517 0.7032967 0.72413793 0.72727273 0.72093023] mean value: 0.72071858811016 key: train_jcc value: [0.81707317 0.80697051 0.80510753 0.79893475 0.8 0.79679144 0.8 0.8046133 0.80241935 0.7978581 ] mean value: 0.8029768155561096 MCC on Blind test: 0.26 MCC on Training: 0.66 Running classifier: 10 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.06434417 0.07428145 0.09719229 0.07637715 0.06981206 0.08657122 0.06770301 0.09130907 0.06865311 0.0797379 ] mean value: 0.07759814262390137 key: score_time value: [0.03212285 0.01373696 0.01977515 0.01268387 0.01303124 0.01319838 0.01307774 0.01329947 0.01676917 0.01305771] mean value: 0.0160752534866333 key: test_mcc value: [0.55986053 0.48859458 0.57215273 0.4669594 0.47041535 0.64442493 0.46672534 0.55587268 0.5338014 0.58532848] mean value: 0.5344135412039934 key: train_mcc value: [0.67329125 0.69811342 0.66681308 0.65902465 0.63306007 0.65194871 0.63808902 0.64113483 0.63768774 0.62752077] mean value: 0.6526683541246874 key: test_fscore value: [0.78873239 0.75862069 0.79136691 0.73529412 0.74647887 0.82352941 0.73913043 0.7761194 0.78378378 0.79710145] mean value: 0.774015746397422 key: train_fscore value: [0.840417 0.85421592 0.83746998 0.8343949 0.81855167 0.82985554 0.82113821 0.82577566 0.82390438 0.81854516] mean value: 0.8304268419076223 key: test_precision value: [0.74666667 0.70512821 0.76388889 0.72463768 0.70666667 0.82352941 0.72857143 0.78787879 0.725 0.78571429] mean value: 0.7497682022439055 key: train_precision value: [0.8200313 0.81996974 0.81591264 0.80864198 0.8099839 0.80907668 0.8105939 0.79846154 0.79783951 0.79503106] mean value: 0.8085542233805636 key: test_recall value: [0.8358209 0.82089552 0.82089552 0.74626866 0.79104478 0.82352941 0.75 0.76470588 0.85294118 0.80882353] mean value: 0.8014925373134328 key: train_recall value: [0.86184211 0.89144737 0.86019737 0.86184211 0.82730263 0.85172982 0.83196046 0.85502471 0.85172982 0.84349259] mean value: 0.8536568975981966 key: test_accuracy value: [0.77777778 0.74074074 0.78518519 0.73333333 0.73333333 0.82222222 0.73333333 0.77777778 0.76296296 0.79259259] mean value: 0.7659259259259259 key: train_accuracy value: [0.83621399 0.84773663 0.83292181 0.82880658 0.81646091 0.8255144 0.81893004 0.81975309 0.818107 0.81316872] mean value: 0.825761316872428 key: test_roc_auc value: [0.77820457 0.74133011 0.78544776 0.73342845 0.73375768 0.82221247 0.73320896 0.77787533 0.76229148 0.79247147] mean value: 0.7660228270412641 key: train_roc_auc value: [0.83619288 0.84770062 0.83289934 0.82877937 0.81645197 0.82553596 0.81894076 0.81978209 0.81813465 0.81319366] mean value: 0.8257611311020548 key: test_jcc value: [0.65116279 0.61111111 0.6547619 0.58139535 0.59550562 0.7 0.5862069 0.63414634 0.64444444 0.6626506 ] mean value: 0.632138505825465 key: train_jcc value: [0.72475795 0.74552957 0.72038567 0.71584699 0.69283747 0.70919067 0.69655172 0.70325203 0.70054201 0.69282815] mean value: 0.7101722241970586 MCC on Blind test: 0.42 MCC on Training: 0.53 Running classifier: 11 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.04954362 0.08630061 0.08647275 0.07284594 0.05341458 0.05404472 0.05393767 0.0532074 0.05478907 0.05319977] mean value: 0.061775612831115725 key: score_time value: [0.01241803 0.01123953 0.01903987 0.01626301 0.01327848 0.01339579 0.0131464 0.01320863 0.01293397 0.01305819] mean value: 0.013798189163208009 key: test_mcc value: [0.51142902 0.37868165 0.52703343 0.46672534 0.48629861 0.54245622 0.45208831 0.51859015 0.54329661 0.61604449] mean value: 0.5042643809738442 key: train_mcc value: [0.55905092 0.57368458 0.56707808 0.60346784 0.59698923 0.56382728 0.59527672 0.57201617 0.57038649 0.54897509] mean value: 0.575075239689405 key: test_fscore value: [0.7480916 0.69565217 0.76811594 0.72727273 0.75524476 0.76335878 0.73381295 0.736 0.78321678 0.8030303 ] mean value: 0.7513796016026275 key: train_fscore value: [0.77666667 0.78612717 0.78389482 0.80422421 0.80161943 0.78296478 0.8 0.78583196 0.78411911 0.77467105] /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( mean value: 0.7880119201549582 key: test_precision value: [0.765625 0.67605634 0.74647887 0.73846154 0.71052632 0.79365079 0.71830986 0.80701754 0.74666667 0.828125 ] mean value: 0.7530917928850657 key: train_precision value: [0.78716216 0.7893864 0.78325123 0.79454254 0.78947368 0.77850163 0.78972713 0.78583196 0.78737542 0.77339901] mean value: 0.7858651161334329 key: test_recall value: [0.73134328 0.71641791 0.79104478 0.71641791 0.80597015 0.73529412 0.75 0.67647059 0.82352941 0.77941176] mean value: 0.7525899912203687 key: train_recall value: [0.76644737 0.78289474 0.78453947 0.81414474 0.81414474 0.78747941 0.81054366 0.78583196 0.78088962 0.77594728] mean value: 0.7902862980143934 key: test_accuracy value: [0.75555556 0.68888889 0.76296296 0.73333333 0.74074074 0.77037037 0.72592593 0.75555556 0.77037037 0.80740741] mean value: 0.7511111111111112 key: train_accuracy value: [0.77942387 0.78683128 0.78353909 0.80164609 0.79835391 0.781893 0.79753086 0.78600823 0.78518519 0.7744856 ] mean value: 0.7874897119341564 key: test_roc_auc value: [0.75537752 0.68909131 0.76316945 0.73320896 0.74122037 0.77063213 0.72574627 0.75614574 0.76997366 0.80761633] mean value: 0.751218173836699 key: train_roc_auc value: [0.77943456 0.78683452 0.78353827 0.8016358 0.7983409 0.7818976 0.79754157 0.78600809 0.78518165 0.7744868 ] mean value: 0.787489974421226 key: test_jcc value: [0.59756098 0.53333333 0.62352941 0.57142857 0.60674157 0.61728395 0.57954545 0.58227848 0.64367816 0.67088608] mean value: 0.6026265988214379 key: train_jcc value: [0.63487738 0.64761905 0.64459459 0.67255435 0.66891892 0.64333782 0.66666667 0.64721845 0.64489796 0.63221477] mean value: 0.6502899956944515 MCC on Blind test: 0.38 MCC on Training: 0.5 Running classifier: 12 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(cv=3, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(cv=3, random_state=42))]) key: fit_time value: [0.75046039 0.90085435 0.67259836 0.67073393 0.76597595 0.69065857 1.05099154 0.87133288 0.68626189 0.68804884] mean value: 0.774791669845581 key: score_time value: [0.01255131 0.0124681 0.01249695 0.01263142 0.01262832 0.01256347 0.01537156 0.01253486 0.01264334 0.01249003] mean value: 0.012837934494018554 key: test_mcc value: [0.60184923 0.45469065 0.51175061 0.49736307 0.48629861 0.63035375 0.51142902 0.61604449 0.6377081 0.6157076 ] mean value: 0.5563195131335331 key: train_mcc value: [0.67882179 0.68573276 0.68025904 0.70277571 0.67488408 0.64335517 0.64958764 0.67413751 0.67413751 0.65763617] mean value: 0.6721327366142391 key: test_fscore value: [0.8057554 0.73758865 0.75912409 0.75362319 0.75524476 0.81203008 0.76258993 0.8030303 0.82993197 0.81428571] mean value: 0.7833204072758172 key: train_fscore value: [0.84394904 0.84761905 0.8443735 0.85624013 0.84109149 0.82485876 0.82668836 0.84168656 0.84168656 0.83373111] mean value: 0.8401924552929984 key: test_precision value: [0.77777778 0.7027027 0.74285714 0.73239437 0.71052632 0.83076923 0.74647887 0.828125 0.7721519 0.79166667] mean value: 0.7635449974733791 key: train_precision value: [0.81790123 0.8190184 0.82015504 0.82370821 0.82131661 0.8085443 0.81672026 0.81384615 0.81384615 0.80615385] mean value: 0.8161210214220909 key: test_recall value: [0.8358209 0.7761194 0.7761194 0.7761194 0.80597015 0.79411765 0.77941176 0.77941176 0.89705882 0.83823529] mean value: 0.8058384547848991 key: train_recall value: [0.87171053 0.87828947 0.87006579 0.89144737 0.86184211 0.84184514 0.8369028 0.87149918 0.87149918 0.86326194] mean value: 0.8658363500390184 key: test_accuracy value: [0.8 0.72592593 0.75555556 0.74814815 0.74074074 0.81481481 0.75555556 0.80740741 0.81481481 0.80740741] mean value: 0.7770370370370371 key: train_accuracy value: [0.83868313 0.84197531 0.83950617 0.85020576 0.83703704 0.82139918 0.82469136 0.83621399 0.83621399 0.82798354] mean value: 0.8353909465020577 key: test_roc_auc value: [0.80026339 0.726295 0.75570676 0.74835382 0.74122037 0.81496927 0.75537752 0.80761633 0.81420105 0.80717735] mean value: 0.7771180860403863 key: train_roc_auc value: [0.83865592 0.8419454 0.839481 0.85017179 0.8370166 0.82141599 0.8247014 0.83624301 0.83624301 0.82801255] mean value: 0.8353886673025231 key: test_jcc value: [0.6746988 0.58426966 0.61176471 0.60465116 0.60674157 0.6835443 0.61627907 0.67088608 0.70930233 0.68674699] mean value: 0.644888466285631 key: train_jcc value: [0.73002755 0.73553719 0.73066298 0.74861878 0.72576177 0.70192308 0.70457698 0.72664835 0.72664835 0.7148704 ] mean value: 0.7245275431376793 MCC on Blind test: 0.46 MCC on Training: 0.56 Running classifier: 13 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [3.96747351 4.45610547 2.96983767 3.45779634 4.55576968 4.09675765 4.37948489 1.99385858 4.37191367 4.99179626] mean value: 3.9240793704986574 key: score_time value: [0.01468015 0.01288676 0.01290631 0.01299262 0.01302457 0.01991773 0.01807237 0.01302886 0.01310992 0.01297784] mean value: 0.014359712600708008 key: test_mcc value: [0.76439627 0.84854147 0.71930611 0.77170765 0.7333187 0.80746434 0.82256727 0.64675445 0.84144718 0.86747328] mean value: 0.7822976732225354 key: train_mcc value: [0.92106661 0.89964821 0.87887141 0.87658468 0.90878809 0.9424191 0.95021115 0.78105973 0.94241832 0.95953689] mean value: 0.9060604205178201 key: test_fscore value: [0.88405797 0.92413793 0.86131387 0.88888889 0.86567164 0.90510949 0.91275168 0.83098592 0.92307692 0.9352518 ] mean value: 0.8931246105376524 key: train_fscore value: [0.96078431 0.95 0.94032258 0.9393223 0.95286195 0.97128794 0.9751004 0.89391576 0.97105045 0.97978981] mean value: 0.9534435516416628 key: test_precision value: [0.85915493 0.85897436 0.84285714 0.83116883 0.86567164 0.89855072 0.83950617 0.7972973 0.88 0.91549296] mean value: 0.8588674056889806 key: train_precision value: [0.95454545 0.9047619 0.92246835 0.90166415 0.97586207 0.96732026 0.95141066 0.84888889 0.97508306 0.96190476] mean value: 0.9363909554954924 key: test_recall value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( [0.91044776 1. 0.88059701 0.95522388 0.86567164 0.91176471 1. 0.86764706 0.97058824 0.95588235] mean value: 0.9317822651448638 key: train_recall value: [0.96710526 1. 0.95888158 0.98026316 0.93092105 0.9752883 1. 0.94398682 0.96705107 0.99835255] mean value: 0.9721849800572271 key: test_accuracy value: [0.88148148 0.91851852 0.85925926 0.88148148 0.86666667 0.9037037 0.9037037 0.82222222 0.91851852 0.93333333] mean value: 0.888888888888889 key: train_accuracy value: [0.96049383 0.9473251 0.93909465 0.93662551 0.95390947 0.97119342 0.9744856 0.88806584 0.97119342 0.97942387] mean value: 0.9521810699588478 key: test_roc_auc value: [0.88169447 0.91911765 0.85941615 0.88202371 0.86665935 0.90364355 0.90298507 0.82188323 0.91812994 0.93316506] mean value: 0.8888718173836698 key: train_roc_auc value: [0.96048838 0.94728171 0.93907835 0.93658957 0.9539284 0.97119678 0.97450658 0.88811183 0.97119001 0.97943943] mean value: 0.9521811053065118 key: test_jcc value: [0.79220779 0.85897436 0.75641026 0.8 0.76315789 0.82666667 0.83950617 0.71084337 0.85714286 0.87837838] mean value: 0.8083287750850634 key: train_jcc value: [0.9245283 0.9047619 0.88736682 0.88558692 0.90996785 0.94417863 0.95141066 0.80818054 0.9437299 0.96038035] mean value: 0.9120091870253864 MCC on Blind test: 0.2 MCC on Training: 0.78 Running classifier: 14 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.0168035 0.01700354 0.01696277 0.01681757 0.01697516 0.01706719 0.01722169 0.01741767 0.01704383 0.0169332 ] mean value: 0.01702461242675781 key: score_time value: [0.0125699 0.01237607 0.01248908 0.01233697 0.01236582 0.01235938 0.01240873 0.0124712 0.01249528 0.01241636] mean value: 0.012428879737854004 key: test_mcc value: [0.40860416 0.39515308 0.40783205 0.43707455 0.1109159 0.39515308 0.39266901 0.41256764 0.51108673 0.37868165] mean value: 0.3849737841821362 key: train_mcc value: [0.40502594 0.45225313 0.45362841 0.42931804 0.36419127 0.40794771 0.44421365 0.40665519 0.41938497 0.31455925] mean value: 0.40971775655824005 key: test_fscore value: [0.6875 0.70921986 0.69230769 0.71212121 0.54545455 0.68217054 0.6962963 0.68253968 0.75912409 0.68181818] mean value: 0.6848552098920538 key: train_fscore value: [0.69217687 0.72040302 0.72379368 0.70766639 0.66838046 0.69543147 0.71404399 0.69224211 0.7021097 0.64206009] mean value: 0.6958307789222878 key: test_precision value: [0.72131148 0.67567568 0.71428571 0.72307692 0.55384615 0.72131148 0.70149254 0.74137931 0.75362319 0.703125 ] mean value: 0.7009127453768197 key: train_precision value: [0.7165493 0.73584906 0.73232323 0.7253886 0.69767442 0.71478261 0.73391304 0.71731449 0.71972318 0.6702509 ] mean value: 0.7163768823597347 key: test_recall value: [0.65671642 0.74626866 0.67164179 0.70149254 0.53731343 0.64705882 0.69117647 0.63235294 0.76470588 0.66176471] mean value: 0.6710491659350308 key: train_recall value: [0.66940789 0.70559211 0.71546053 0.69078947 0.64144737 0.67710049 0.69522241 0.66886326 0.68533773 0.61614498] mean value: 0.6765366231682997 key: test_accuracy value: [0.7037037 0.6962963 0.7037037 0.71851852 0.55555556 0.6962963 0.6962963 0.7037037 0.75555556 0.68888889] mean value: 0.6918518518518518 key: train_accuracy value: [0.70205761 0.72592593 0.72674897 0.71440329 0.68148148 0.7037037 0.7218107 0.70288066 0.70946502 0.65679012] mean value: 0.7045267489711934 key: test_roc_auc value: [0.70335821 0.69666374 0.70346795 0.71839333 0.55542142 0.69666374 0.6963345 0.70423617 0.75548727 0.68909131] mean value: 0.6919117647058824 key: train_roc_auc value: [0.70208451 0.72594268 0.72675827 0.71442274 0.68151446 0.70368183 0.72178883 0.70285268 0.70944518 0.6567567 ] mean value: 0.7045247875661147 key: test_jcc value: [0.52380952 0.54945055 0.52941176 0.55294118 0.375 0.51764706 0.53409091 0.51807229 0.61176471 0.51724138] mean value: 0.5229429356700306 key: train_jcc value: [0.52925878 0.56299213 0.56714472 0.54758801 0.5019305 0.53307393 0.55526316 0.52933507 0.54096229 0.47281922] mean value: 0.5340367794882693 MCC on Blind test: 0.1 MCC on Training: 0.38 Running classifier: 15 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.01800513 0.01780725 0.01774693 0.01796985 0.01786232 0.01784229 0.01786971 0.01955891 0.0185585 0.01946402] mean value: 0.018268489837646486 key: score_time value: [0.01265121 0.01263785 0.01262403 0.01265669 0.01268792 0.01269484 0.01256394 0.01281285 0.01293969 0.0128808 ] mean value: 0.012714982032775879 key: test_mcc value: [0.39274759 0.32019554 0.33695759 0.25949507 0.18559196 0.42230026 0.42542573 0.33384589 0.43752856 0.20127835] mean value: 0.3315366537329184 key: train_mcc value: [0.3598299 0.4013539 0.37615032 0.37778217 0.36303154 0.38829066 0.36664016 0.37949491 0.38468203 0.37777772] mean value: 0.3775033324411248 key: test_fscore value: [0.6870229 0.67142857 0.68531469 0.63235294 0.59854015 0.71111111 0.73103448 0.66165414 0.72857143 0.58461538] mean value: 0.6691645787063378 key: train_fscore value: [0.68502024 0.70833333 0.6901063 0.69016393 0.67883817 0.70192308 0.68976632 0.6922449 0.69789984 0.68811881] mean value: 0.692241492364032 key: test_precision value: [0.703125 0.64383562 0.64473684 0.62318841 0.58571429 0.71641791 0.68831169 0.67692308 0.70833333 0.61290323] mean value: 0.6603489384877318 key: train_precision value: [0.67464115 0.690625 0.68617886 0.6879085 0.68509213 0.68330733 0.67507886 0.68608414 0.68462758 0.6892562 ] mean value: 0.6842799746815056 key: test_recall value: [0.67164179 0.70149254 0.73134328 0.64179104 0.6119403 0.70588235 0.77941176 0.64705882 0.75 0.55882353] mean value: 0.6799385425812116 key: train_recall value: [0.69572368 0.72697368 0.69407895 0.69243421 0.67269737 0.72158155 0.70510708 0.6985173 0.71169687 0.68698517] mean value: 0.70057958683777 key: test_accuracy value: [0.6962963 0.65925926 0.66666667 0.62962963 0.59259259 0.71111111 0.71111111 0.66666667 0.71851852 0.6 ] mean value: 0.6651851851851852 key: train_accuracy value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1327: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior. _warn_prf(average, modifier, msg_start, len(result)) /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") [0.67983539 0.70041152 0.68806584 0.68888889 0.68148148 0.69382716 0.68312757 0.68971193 0.69218107 0.68888889] mean value: 0.6886419753086421 key: test_roc_auc value: [0.69611501 0.6595698 0.66714223 0.62971905 0.59273486 0.71115013 0.7106014 0.66681299 0.71828358 0.60030729] mean value: 0.6652436347673398 key: train_roc_auc value: [0.6798223 0.70038964 0.68806089 0.68888597 0.68148872 0.69384998 0.68314565 0.68971918 0.69219712 0.68888732] mean value: 0.6886446772305559 key: test_jcc value: [0.52325581 0.50537634 0.5212766 0.46236559 0.42708333 0.55172414 0.57608696 0.49438202 0.57303371 0.41304348] mean value: 0.5047627981566095 key: train_jcc value: [0.52093596 0.5483871 0.52684145 0.52690864 0.5138191 0.54074074 0.52644526 0.52933833 0.53598015 0.5245283 ] mean value: 0.5293925019881908 MCC on Blind test: -0.0 MCC on Training: 0.33 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.02582669 0.02608633 0.02612758 0.03267121 0.03499937 0.03129339 0.03773475 0.03740096 0.02683997 0.03319669] mean value: 0.03121769428253174 key: score_time value: [0.01239014 0.01238465 0.01254487 0.01233721 0.01239252 0.01238203 0.01244283 0.01232505 0.01238513 0.01241493] mean value: 0.012399935722351074 key: test_mcc value: [0.41319355 0.30904033 0.33171128 0.42218018 0.33210575 0.19689685 0.44451782 0.48950982 0.555225 0.24549114] mean value: 0.37398717042769986 key: train_mcc value: [0.53031273 0.27813546 0.44229282 0.42293308 0.27614119 0.28726788 0.45493613 0.50553006 0.52685888 0.43388382] mean value: 0.41582920414446667 key: test_fscore value: [0.67213115 0.34146341 0.52 0.56565657 0.71351351 0.68783069 0.75138122 0.64150943 0.79746835 0.6984127 ] mean value: 0.6389367031450852 key: train_fscore value: [0.73617407 0.32751678 0.5974026 0.54857143 0.70229008 0.703207 0.75407779 0.66938776 0.77994012 0.74888748] mean value: 0.656745509137584 key: test_precision value: [0.74545455 0.93333333 0.78787879 0.875 0.55932203 0.53719008 0.60176991 0.89473684 0.7 0.54545455] mean value: 0.7180140082273834 key: train_precision value: [0.82020202 0.89051095 0.87341772 0.8988764 0.54611872 0.54422383 0.60891591 0.87935657 0.71467764 0.60973085] mean value: 0.7386030607914196 key: test_recall value: [0.6119403 0.20895522 0.3880597 0.41791045 0.98507463 0.95588235 1. 0.5 0.92647059 0.97058824] mean value: 0.6964881474978051 key: train_recall value: [0.66776316 0.20065789 0.45394737 0.39473684 0.98355263 0.99341021 0.99011532 0.54036244 0.8583196 0.97034596] mean value: 0.7053211436746727 key: test_accuracy value: [0.7037037 0.6 0.64444444 0.68148148 0.60740741 0.56296296 0.66666667 0.71851852 0.76296296 0.57777778] mean value: 0.6525925925925925 key: train_accuracy value: [0.76049383 0.58765432 0.69382716 0.67489712 0.58271605 0.58106996 0.67736626 0.73333333 0.75802469 0.67489712] mean value: 0.6724279835390947 key: test_roc_auc value: [0.70302897 0.59712467 0.64255926 0.67954346 0.61018437 0.56003073 0.6641791 0.72014925 0.76174276 0.57484636] mean value: 0.6513388937664618 key: train_roc_auc value: [0.76057021 0.5879731 0.69402476 0.67512789 0.58238587 0.58140905 0.67762345 0.73317464 0.75810717 0.67514009] mean value: 0.6725536232983612 key: test_jcc value: [0.50617284 0.20588235 0.35135135 0.3943662 0.55462185 0.52419355 0.60176991 0.47222222 0.66315789 0.53658537] mean value: 0.48103235324255406 key: train_jcc value: [0.58249641 0.19582665 0.42592593 0.37795276 0.54117647 0.54226619 0.60523666 0.50306748 0.6392638 0.59857724] mean value: 0.5011789578646396 MCC on Blind test: 0.0 MCC on Training: 0.37 Running classifier: 17 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.04254699 0.04103184 0.04152751 0.04345989 0.04095769 0.04253411 0.0631578 0.07201719 0.05064368 0.04948878] mean value: 0.04873654842376709 key: score_time value: [0.01326799 0.01324272 0.01369071 0.01333165 0.01317072 0.01363373 0.01398611 0.01392007 0.01407027 0.016711 ] mean value: 0.013902497291564942 key: test_mcc value: [0.5532728 0.58724181 0.47321293 0.5184352 0.53057761 0.48027581 0.44213626 0.48027581 0.58451522 0.53855203] mean value: 0.5188495499512062 key: train_mcc value: [0.55403653 0.58702071 0.49912429 0.52460348 0.51581257 0.51624188 0.49958865 0.49444342 0.54932111 0.59903519] mean value: 0.5339227821685671 key: test_fscore value: [0.78823529 0.80239521 0.75706215 0.77647059 0.77906977 0.76404494 0.75280899 0.76404494 0.80473373 0.78823529] mean value: 0.7777100904601087 key: train_fscore value: [0.79063719 0.8042328 0.76913346 0.77920411 0.7755102 0.7752235 0.76884104 0.76689829 0.78831169 0.80965147] mean value: 0.7827643762351644 key: test_precision value: [0.65048544 0.67 0.60909091 0.6407767 0.63809524 0.61818182 0.60909091 0.61818182 0.67326733 0.65686275] mean value: 0.6384032900393735 key: train_precision value: [0.65376344 0.67256637 0.62487153 0.63894737 0.63333333 0.63295099 0.6244856 0.62192623 0.6505895 0.68248588] mean value: 0.6435920234428483 key: test_recall value: [1. 1. 1. 0.98507463 1. 1. 0.98529412 1. 1. 0.98529412] mean value: 0.9955662862159788 key: train_recall value: [1. 1. 1. 0.99835526 1. 1. 1. 1. 1. 0.99505766] mean value: 0.9993412923783923 key: test_accuracy value: [0.73333333 0.75555556 0.68148148 0.71851852 0.71851852 0.68888889 0.67407407 0.68888889 0.75555556 0.73333333] mean value: 0.7148148148148149 key: train_accuracy value: [0.73497942 0.7563786 0.69958848 0.71687243 0.71028807 0.71028807 0.69958848 0.6962963 0.73168724 0.76625514] mean value: 0.7222222222222222 key: test_roc_auc value: [0.73529412 0.75735294 0.68382353 0.72047849 0.72058824 0.68656716 0.67175154 0.68656716 0.75373134 0.73145303] mean value: 0.714760755048288 key: train_roc_auc value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( [0.73476112 0.75617792 0.69934102 0.71664056 0.71004942 0.71052632 0.69983553 0.69654605 0.73190789 0.7664433 ] mean value: 0.722222914679615 key: test_jcc value: [0.65048544 0.67 0.60909091 0.63461538 0.63809524 0.61818182 0.6036036 0.61818182 0.67326733 0.65048544] mean value: 0.6366006972287852 key: train_jcc value: [0.65376344 0.67256637 0.62487153 0.6382755 0.63333333 0.63295099 0.6244856 0.62192623 0.6505895 0.68018018] mean value: 0.6432942669955634 MCC on Blind test: 0.13 MCC on Training: 0.52 Running classifier: 18 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42))]) key: fit_time value: [0.91733289 0.92580128 0.91950941 0.87493587 1.00399446 0.9585464 0.92418003 0.92398477 0.94713473 0.92493033] mean value: 0.9320350170135498 key: score_time value: [0.20353174 0.22401977 0.17749214 0.19373703 0.17998314 0.19692969 0.20615149 0.15648222 0.23207355 0.18299294] mean value: 0.19533936977386473 key: test_mcc value: [0.9424184 0.9565124 0.9565124 0.98529412 0.91478147 0.98529091 0.89870563 1. 0.95648435 0.98529091] mean value: 0.9581290589640435 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.97101449 0.97810219 0.97810219 0.99259259 0.95714286 0.99270073 0.95035461 1. 0.97841727 0.99270073] mean value: 0.9791127658021258 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.94366197 0.95714286 0.95714286 0.98529412 0.91780822 0.98550725 0.91780822 1. 0.95774648 0.98550725] mean value: 0.9607619213746788 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 0.98529412 1. 1. 1. ] mean value: 0.9985294117647058 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.97037037 0.97777778 0.97777778 0.99259259 0.95555556 0.99259259 0.94814815 1. 0.97777778 0.99259259] mean value: 0.9785185185185185 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.97058824 0.97794118 0.97794118 0.99264706 0.95588235 0.99253731 0.94787094 1. 0.97761194 0.99253731] mean value: 0.9785557506584723 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.94366197 0.95714286 0.95714286 0.98529412 0.91780822 0.98550725 0.90540541 1. 0.95774648 0.98550725] mean value: 0.9595216399974111 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.31 MCC on Training: 0.96 Running classifier: 19 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea...age_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42))]) key: fit_time value: [1.31263733 1.39705348 1.3307426 1.32326531 1.29258657 1.30801749 1.34421682 1.2933948 1.29255271 1.36207557] mean value: 1.3256542682647705 key: score_time value: [0.25319934 0.24114871 0.2516377 0.24552464 0.23190737 0.28773475 0.29955721 0.28233075 0.26506186 0.26806831] mean value: 0.2626170635223389 key: test_mcc value: [0.85184372 0.88505217 0.88147498 0.85218556 0.87145367 0.92601539 0.83780293 0.97036874 0.92681399 0.91152582] mean value: 0.8914536969348774 key: train_mcc value: [0.98190489 0.98192613 0.97532042 0.97201772 0.98353908 0.98189435 0.97697602 0.97861283 0.98200098 0.98358719] mean value: 0.979777960601961 key: test_fscore value: [0.92537313 0.94285714 0.94029851 0.92424242 0.93617021 0.96350365 0.92086331 0.98529412 0.96402878 0.95522388] mean value: 0.9457855155866615 key: train_fscore value: [0.99097621 0.99099099 0.98769483 0.98603122 0.99177632 0.9909465 0.98850575 0.989318 0.99100572 0.99180328] mean value: 0.9899048819512976 key: test_precision value: [0.92537313 0.90410959 0.94029851 0.93846154 0.89189189 0.95652174 0.90140845 0.98529412 0.94366197 0.96969697] mean value: 0.9356717910195247 key: train_precision value: [0.98854337 0.98694943 0.98527005 0.98522167 0.99177632 0.99013158 0.98527005 0.98688525 0.98376623 0.98694943] mean value: 0.9870763377078372 key: test_recall value: [0.92537313 0.98507463 0.94029851 0.91044776 0.98507463 0.97058824 0.94117647 0.98529412 0.98529412 0.94117647] mean value: 0.9569798068481123 key: train_recall value: [0.99342105 0.99506579 0.99013158 0.98684211 0.99177632 0.99176277 0.99176277 0.99176277 0.99835255 0.99670511] mean value: 0.9927582805861441 key: test_accuracy value: [0.92592593 0.94074074 0.94074074 0.92592593 0.93333333 0.96296296 0.91851852 0.98518519 0.96296296 0.95555556] mean value: 0.9451851851851852 key: train_accuracy value: [0.9909465 0.9909465 0.98765432 0.98600823 0.99176955 0.9909465 0.98847737 0.98930041 0.9909465 0.99176955] mean value: 0.9898765432098765 key: test_roc_auc value: [0.92592186 0.94106673 0.94073749 0.92581212 0.93371378 0.96290606 0.91834943 0.98518437 0.96279631 0.95566286] mean value: 0.9452151009657594 key: train_roc_auc value: [0.99094446 0.99094311 0.98765228 0.98600754 0.99176954 0.99094717 0.98848007 0.98930244 0.99095259 0.99177361] mean value: 0.989877281496575 key: test_jcc value: [0.86111111 0.89189189 0.88732394 0.85915493 0.88 0.92957746 0.85333333 0.97101449 0.93055556 0.91428571] mean value: 0.89782484369594 key: train_jcc value: [0.98211382 0.98214286 0.97568882 0.97244733 0.98368679 0.98205546 0.97727273 0.97886179 0.9821718 0.98373984] mean value: 0.9800181224446707 MCC on Blind test: 0.45 MCC on Training: 0.89 Running classifier: 20 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.0422008 0.04269433 0.03871679 0.04211736 0.04272985 0.04264379 0.02009702 0.02005386 0.05449343 0.03786731] mean value: 0.03836145401000977 key: score_time value: [0.0193665 0.01956844 0.02008891 0.01932311 0.019557 0.01337361 0.01249123 0.01253271 0.01994967 0.02017808] mean value: 0.017642927169799805 key: test_mcc value: [0.57105218 0.43911272 0.60070296 0.51108673 0.4669594 0.54072449 0.49637612 0.58775921 0.51742784 0.6000878 ] mean value: 0.5331289451887241 key: train_mcc value: [0.63951226 0.62806335 0.61178152 0.64662611 0.60823454 0.63307299 0.62644546 0.63001642 0.63647136 0.61018481] mean value: 0.6270408830438352 key: test_fscore value: [0.78832117 0.72857143 0.80291971 0.7518797 0.73529412 0.77372263 0.75362319 0.78461538 0.7755102 0.8 ] mean value: 0.7694457526219056 key: train_fscore value: [0.82034454 0.81566069 0.80875203 0.82675262 0.80395387 0.81825591 0.81469388 0.81781377 0.82047116 0.80778589] mean value: 0.815448434733933 key: test_precision value: [0.77142857 0.69863014 0.78571429 0.75757576 0.72463768 0.76811594 0.74285714 0.82258065 0.72151899 0.80597015] mean value: 0.759902929950726 key: train_precision value: [0.81833061 0.80906149 0.7971246 0.81042654 0.80528053 0.80967742 0.80744337 0.80414013 0.80929487 0.79552716] mean value: 0.806630670399749 key: test_recall value: [0.80597015 0.76119403 0.82089552 0.74626866 0.74626866 0.77941176 0.76470588 0.75 0.83823529 0.79411765] mean value: 0.7807067603160667 key: train_recall value: [0.82236842 0.82236842 0.82072368 0.84375 0.80263158 0.82701812 0.82207578 0.83196046 0.83196046 0.82042834] mean value: 0.8245285268360357 key: test_accuracy value: [0.78518519 0.71851852 0.8 0.75555556 0.73333333 0.77037037 0.74814815 0.79259259 0.75555556 0.8 ] mean value: 0.7659259259259259 key: train_accuracy value: [0.81975309 0.81399177 0.80576132 0.82304527 0.80411523 0.81646091 0.81316872 0.81481481 0.818107 0.80493827] mean value: 0.8134156378600823 key: test_roc_auc value: [0.78533802 0.71883231 0.80015364 0.75548727 0.73342845 0.7703029 0.74802458 0.79291045 0.75493854 0.8000439 ] mean value: 0.7659460052677787 key: train_roc_auc value: [0.81975093 0.81398487 0.80574899 0.82302821 0.80411645 0.81646959 0.81317605 0.81482891 0.81811839 0.80495101] mean value: 0.8134173404578167 key: test_jcc value: [0.65060241 0.57303371 0.67073171 0.60240964 0.58139535 0.63095238 0.60465116 0.64556962 0.63333333 0.66666667] mean value: 0.6259345976208466 key: train_jcc value: [0.69541029 0.68870523 0.67891156 0.70467033 0.67217631 0.69241379 0.68732782 0.69178082 0.69559229 0.67755102] mean value: 0.6884539474690485 MCC on Blind test: 0.55 MCC on Training: 0.53 Running classifier: 21 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=3))]) key: fit_time value: [0.0702827 0.15567088 0.16777849 0.26479149 0.18171883 0.1657095 0.14562988 0.07069111 0.12875748 0.1590848 ] mean value: 0.15101151466369628 key: score_time value: [0.01245618 0.01951694 0.01943922 0.02650642 0.01937318 0.01943111 0.01241946 0.01245999 0.02275467 0.01924014] mean value: 0.018359732627868653 key: test_mcc value: [0.55802991 0.43911272 0.58637414 0.48209982 0.54245622 0.6000878 0.49637612 0.55587268 0.51742784 0.57036225] mean value: 0.5348199502520774 key: train_mcc value: [0.6582996 0.62806335 0.650012 0.66169872 0.64127131 0.64730962 0.62644546 0.66393846 0.64217499 0.61860205] mean value: 0.6437815568721048 key: test_fscore value: [0.78571429 0.72857143 0.79710145 0.74452555 0.77697842 0.8 0.75362319 0.7761194 0.7755102 0.78832117] mean value: 0.7726465091628236 key: train_fscore value: [0.83279743 0.81566069 0.82864039 0.83467095 0.82247557 0.82813749 0.81469388 0.83639266 0.82532051 0.81260097] mean value: 0.8251390523339855 key: test_precision value: [0.75342466 0.69863014 0.77464789 0.72857143 0.75 0.80597015 0.74285714 0.78787879 0.72151899 0.7826087 ] mean value: 0.7546107873399529 key: train_precision value: [0.81446541 0.80906149 0.81102362 0.81504702 0.81451613 0.80434783 0.80744337 0.81114551 0.80343214 0.79714739] mean value: 0.8087629895508412 key: test_recall value: [0.82089552 0.76119403 0.82089552 0.76119403 0.80597015 0.79411765 0.76470588 0.76470588 0.83823529 0.79411765] mean value: 0.7926031606672519 key: train_recall value: [0.85197368 0.82236842 0.84703947 0.85526316 0.83059211 0.85337727 0.82207578 0.86326194 0.84843493 0.82866557] mean value: 0.8423052328101968 key: test_accuracy value: [0.77777778 0.71851852 0.79259259 0.74074074 0.77037037 0.8 0.74814815 0.77777778 0.75555556 0.78518519] mean value: 0.7666666666666668 key: train_accuracy value: [0.82880658 0.81399177 0.82469136 0.83045267 0.82057613 0.82304527 0.81316872 0.83127572 0.82057613 0.8090535 ] mean value: 0.8215637860082305 key: test_roc_auc value: [0.77809482 0.71883231 0.7928007 0.74089113 0.77063213 0.8000439 0.74802458 0.77787533 0.75493854 0.78511853] mean value: 0.7667251975417032 key: train_roc_auc value: [0.8287875 0.81398487 0.82467295 0.83043224 0.82056788 0.82307021 0.81317605 0.83130202 0.82059904 0.80906963] mean value: 0.821566239269921 key: test_jcc value: [0.64705882 0.57303371 0.6626506 0.59302326 0.63529412 0.66666667 0.60465116 0.63414634 0.63333333 0.65060241] mean value: 0.6300460421157899 key: train_jcc value: [0.71349862 0.68870523 0.70741758 0.71625344 0.69847856 0.70668486 0.68732782 0.71879287 0.70259209 0.68435374] mean value: 0.7024104820436724 MCC on Blind test: 0.51 MCC on Training: 0.53 Running classifier: 22 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.09805346 0.10076761 0.08860874 0.08483458 0.08279705 0.08134246 0.08085084 0.10179234 0.09741974 0.10101342] mean value: 0.09174802303314208 key: score_time value: [0.02804685 0.0292635 0.02972698 0.02989554 0.02897978 0.02873755 0.02867508 0.0303266 0.03135705 0.03090477] mean value: 0.02959136962890625 key: test_mcc value: [0.52767931 0.55553117 0.5556777 0.60292672 0.49736307 0.66124225 0.55631459 0.5785813 0.51175061 0.65935031] mean value: 0.5706417014239025 key: train_mcc value: [0.65548944 0.71687535 0.65493298 0.68242092 0.68900627 0.68342454 0.6874743 0.67742128 0.64211646 0.64298689] mean value: 0.673214843548419 key: test_fscore value: [0.75 0.7761194 0.77272727 0.78740157 0.75362319 0.82442748 0.78571429 0.768 0.7518797 0.82962963] mean value: 0.7799522534429361 key: train_fscore value: [0.8220339 0.85878489 0.82352941 0.83983402 0.84315353 0.83630195 0.84140234 0.83747927 0.811044 0.81901585] mean value: 0.833257916354427 key: test_precision value: [0.78688525 0.7761194 0.78461538 0.83333333 0.73239437 0.85714286 0.76388889 0.84210526 0.76923077 0.8358209 ] mean value: 0.7981536406975412 key: train_precision value: [0.8479021 0.85737705 0.8419244 0.84757119 0.85092127 0.86188811 0.85279188 0.84307179 0.85144928 0.82939189] mean value: 0.848428895164484 key: test_recall value: [0.71641791 0.7761194 0.76119403 0.74626866 0.7761194 0.79411765 0.80882353 0.70588235 0.73529412 0.82352941] mean value: 0.7643766461808603 key: train_recall value: [0.79769737 0.86019737 0.80592105 0.83223684 0.83552632 0.8121911 0.83031301 0.83196046 0.77429984 0.80889621] mean value: 0.8189239573398075 key: test_accuracy value: [0.76296296 0.77777778 0.77777778 0.8 0.74814815 0.82962963 0.77777778 0.78518519 0.75555556 0.82962963] mean value: 0.7844444444444445 key: train_accuracy value: [0.82716049 0.85843621 0.82716049 0.84115226 0.84444444 0.84115226 0.8436214 0.83868313 0.81975309 0.82139918] mean value: 0.8362962962962964 key: test_roc_auc value: [0.76262072 0.77776558 0.77765584 0.79960492 0.74835382 0.82989464 0.77754609 0.785777 0.75570676 0.82967515] mean value: 0.7844600526777874 key: train_roc_auc value: [0.82718476 0.85843476 0.82717799 0.84115961 0.84445179 0.84112845 0.84361045 0.8386776 0.81971571 0.82138889] mean value: 0.8362930016040926 key: test_jcc value: [0.6 0.63414634 0.62962963 0.64935065 0.60465116 0.7012987 0.64705882 0.62337662 0.60240964 0.70886076] mean value: 0.6400782329487016 key: train_jcc value: [0.69784173 0.75251799 0.7 0.72389127 0.72883788 0.71865889 0.72622478 0.72039943 0.68214804 0.69350282] mean value: 0.7144022832965679 MCC on Blind test: 0.31 MCC on Training: 0.57 Running classifier: 23 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.04697037 0.05362177 0.05435276 0.0632174 0.04261923 0.04052067 0.0513382 0.04461336 0.05585384 0.04872537] mean value: 0.05018329620361328 key: score_time value: [0.01078677 0.01250911 0.01263142 0.01254058 0.01257849 0.01262975 0.01316619 0.01248026 0.01269293 0.01249647] mean value: 0.01245119571685791 key: test_mcc value: [0.56220931 0.3367214 0.32771761 0.40103705 0.08343808 0.54477014 0.20592365 0.49447238 0.40772267 0.31050052] mean value: 0.36745128146465733 key: train_mcc value: [0.60798249 0.25181424 0.31124392 0.49243276 0.25912001 0.53517114 0.25973977 0.52483512 0.46417845 0.51607803] mean value: 0.4222595934284431 key: test_fscore value: [0.79166667 0.4137931 0.45652174 0.73684211 0.17948718 0.79220779 0.25 0.66055046 0.73913043 0.71264368] mean value: 0.5732843157862632 key: train_fscore value: [0.81570997 0.30581867 0.38610039 0.77043819 0.2651622 0.78758621 0.29005525 0.6988189 0.75950999 0.77979798] mean value: 0.5858997751456905 key: test_precision value: [0.74025974 0.9 0.84 0.60576923 0.63636364 0.70930233 0.83333333 0.87804878 0.5862069 0.58490566] mean value: 0.7314189603724224 key: train_precision value: [0.75418994 0.86259542 0.88757396 0.63952226 0.93069307 0.67734282 0.8974359 0.86797066 0.62394068 0.6594533 ] mean value: 0.7800718017960416 key: test_recall value: [0.85074627 0.26865672 0.31343284 0.94029851 0.10447761 0.89705882 0.14705882 0.52941176 1. 0.91176471] mean value: 0.5962906057945567 key: train_recall value: [0.88815789 0.18585526 0.24671053 0.96875 0.15460526 0.94069193 0.17298188 0.58484349 0.97034596 0.9538715 ] mean value: 0.6066813708488685 key: test_accuracy value: [0.77777778 0.62222222 0.62962963 0.66666667 0.52592593 0.76296296 0.55555556 0.72592593 0.64444444 0.62962963] mean value: 0.6540740740740741 key: train_accuracy value: [0.79917695 0.57777778 0.60740741 0.71111111 0.57119342 0.74650206 0.57695473 0.74814815 0.69300412 0.7308642 ] mean value: 0.6762139917695472 key: test_roc_auc value: [0.77831431 0.61962248 0.62730465 0.66867867 0.52282704 0.76196225 0.55860404 0.72739245 0.64179104 0.62752414] mean value: 0.6534021071115013 key: train_roc_auc value: [0.79910366 0.57810061 0.60770452 0.71089889 0.57153657 0.74666175 0.57662252 0.74801385 0.69323219 0.73104759] mean value: 0.6762922158154859 key: test_jcc value: [0.65517241 0.26086957 0.29577465 0.58333333 0.09859155 0.65591398 0.14285714 0.49315068 0.5862069 0.55357143] mean value: 0.4325441640933353 key: train_jcc value: [0.68877551 0.18051118 0.23923445 0.62659574 0.15284553 0.64960182 0.16962843 0.53706505 0.61226611 0.63907285] mean value: 0.44955966813145826 MCC on Blind test: 0.38 MCC on Training: 0.37 Running classifier: 24 Model_name: XGBoost Model func: /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:463: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_CV['source_data'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:490: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_BT['source_data'] = 'BT' XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.29433322 0.18400526 0.20388865 0.18002009 0.1836102 0.51942801 0.63635159 0.18612885 0.22714305 0.22117138] mean value: 0.2836080312728882 key: score_time value: [0.01250839 0.01210499 0.01162601 0.01185107 0.01172447 0.01364946 0.01210928 0.01216674 0.01286387 0.01147318] mean value: 0.012207746505737305 key: test_mcc value: [0.9565124 0.97080134 0.9424184 0.97080134 0.92851083 0.98529091 0.92681399 0.98529091 0.9423692 1. ] mean value: 0.9608809320259212 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.97810219 0.98529412 0.97101449 0.98529412 0.96402878 0.99270073 0.96402878 0.99270073 0.97142857 1. ] mean value: 0.9804592503068182 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.95714286 0.97101449 0.94366197 0.97101449 0.93055556 0.98550725 0.94366197 0.98550725 0.94444444 1. ] mean value: 0.9632510279065698 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 0.98529412 1. 1. 1. ] mean value: 0.9985294117647058 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.97777778 0.98518519 0.97037037 0.98518519 0.96296296 0.99259259 0.96296296 0.99259259 0.97037037 1. ] mean value: 0.9799999999999999 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.97794118 0.98529412 0.97058824 0.98529412 0.96323529 0.99253731 0.96279631 0.99253731 0.97014925 1. ] mean value: 0.9800373134328358 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.95714286 0.97101449 0.94366197 0.97101449 0.93055556 0.98550725 0.93055556 0.98550725 0.94444444 1. ] mean value: 0.9619403862790268 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.62 MCC on Training: 0.96 Extracting tts_split_name: sl Total cols in each df: CV df: 8 metaDF: 17 Adding column: Model_name Total cols in bts df: BT_df: 8 First proceeding to rowbind CV and BT dfs: Final output should have: 25 columns Combinig 2 using pd.concat by row ~ rowbind Checking Dims of df to combine: Dim of CV: (24, 8) Dim of BT: (24, 8) 8 Number of Common columns: 8 These are: ['source_data', 'Precision', 'JCC', 'MCC', 'Recall', 'Accuracy', 'F1', 'ROC_AUC'] Concatenating dfs with different resampling methods [WF]: Split type: sl No. of dfs combining: 2 PASS: 2 dfs successfully combined nrows in combined_df_wf: 48 ncols in combined_df_wf: 8 PASS: proceeding to merge metadata with CV and BT dfs Adding column: Model_name ========================================================= SUCCESS: Ran multiple classifiers ======================================================= ============================================================== Running several classification models (n): 24 List of models: ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('Gaussian NB', GaussianNB()) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('K-Nearest Neighbors', KNeighborsClassifier()) ('LDA', LinearDiscriminantAnalysis()) ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(cv=3, random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Multinomial', MultinomialNB()) ('Naive Bayes', BernoulliNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=12, random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Random Forest', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42)) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=3)) ('SVC', SVC(random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=12, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ================================================================ Running classifier: 1 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. 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Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... 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[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.7s remaining: 1.3s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.7s remaining: 1.3s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.7s remaining: 1.4s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.7s remaining: 1.4s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.7s remaining: 1.4s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.7s remaining: 1.4s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.7s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.7s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.7s remaining: 0.2s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.7s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.7s remaining: 1.5s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.8s remaining: 1.5s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.7s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.7s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.8s remaining: 0.3s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.8s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.8s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.8s remaining: 1.5s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.8s remaining: 0.3s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.8s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.8s remaining: 0.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.8s remaining: 0.3s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.8s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.8s remaining: 0.3s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.8s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.8s remaining: 1.6s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.8s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.8s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.8s remaining: 0.3s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.8s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. 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[Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.12150407 0.11781502 0.11915851 0.11515284 0.12424064 0.12252641 0.12395263 0.12432814 0.12378836 0.12302208] mean value: 0.12154886722564698 key: score_time value: [0.01508117 0.01541305 0.01521039 0.01524925 0.01585293 0.0162735 0.01604438 0.01617146 0.01594782 0.01584959] mean value: 0.015709352493286134 key: test_mcc value: [0.16903085 0.2508726 0. 0.41812101 0.33371191 0.48075018 0.30240737 0.04545455 0.31298622 0.41096386] mean value: 0.27242985324697183 key: train_mcc value: [1. 0.98113038 1. 0.99052111 1. 1. 1. 1. 0.96225401 0.97195268] mean value: 0.9905858181250601 key: test_fscore value: [0.54545455 0.60869565 0.53846154 0.72 0.69230769 0.7 0.6 0.52173913 0.63636364 0.66666667] mean value: 0.6229688861862775 key: train_fscore value: [1. 0.99038462 1. 0.99526066 1. 1. 1. 1. 0.98076923 0.98550725] mean value: 0.9951921756037766 key: test_precision value: [0.6 0.63636364 0.5 0.69230769 0.6 0.77777778 0.66666667 0.54545455 0.7 0.77777778] mean value: 0.6496348096348097 key: train_precision value: [1. 1. 1. 0.99056604 1. 1. 1. 1. 0.99029126 1. ] mean value: 0.9980857299871773 key: test_recall value: [0.5 0.58333333 0.58333333 0.75 0.81818182 0.63636364 0.54545455 0.5 0.58333333 0.58333333] mean value: 0.6083333333333333 key: train_recall value: [1. 0.98095238 1. 1. 1. 1. 1. 1. 0.97142857 0.97142857] mean value: 0.9923809523809524 key: test_accuracy value: [0.58333333 0.625 0.5 0.70833333 0.65217391 0.73913043 0.65217391 0.52173913 0.65217391 0.69565217] mean value: 0.6329710144927537 key: train_accuracy value: [1. 0.99047619 1. 0.9952381 1. 1. 1. 1. 0.98104265 0.98578199] mean value: 0.9952538930264048 key: test_roc_auc value: [0.58333333 0.625 0.5 0.70833333 0.65909091 0.73484848 0.64772727 0.52272727 0.65530303 0.70075758] mean value: 0.6337121212121213 key: train_roc_auc value: [1. 0.99047619 1. 0.9952381 1. 1. 1. 1. 0.9809973 0.98571429] mean value: 0.9952425876010782 key: test_jcc value: [0.375 0.4375 0.36842105 0.5625 0.52941176 0.53846154 0.42857143 0.35294118 0.46666667 0.5 ] mean value: 0.45594736275076836 key: train_jcc value: [1. 0.98095238 1. 0.99056604 1. 1. 1. 1. 0.96226415 0.97142857] mean value: 0.9905211141060197 MCC on Blind test: 0.32 MCC on Training: 0.27 Running classifier: 2 Model_name: Bagging Classifier Model func: BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3))]) key: fit_time value: [0.17067695 0.195678 0.17490554 0.18439007 0.19088936 0.17710924 0.16803908 0.19141984 0.19121265 0.18875289] mean value: 0.18330736160278321 key: score_time value: [0.04988575 0.05554843 0.06141138 0.05009437 0.0444653 0.06450629 0.07917857 0.04909253 0.03968358 0.0584662 ] mean value: 0.05523324012756348 key: test_mcc value: [0.41812101 0.43033148 0.16666667 0.2508726 0.56818182 0.39727608 0.56490196 0.12878788 0.39393939 0.44411739] mean value: 0.37631962767545646 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.69565217 0.66666667 0.58333333 0.64 0.7826087 0.63157895 0.76190476 0.58333333 0.69565217 0.63157895] mean value: 0.6672309033453198 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.72727273 0.77777778 0.58333333 0.61538462 0.75 0.75 0.8 0.58333333 0.72727273 0.85714286] mean value: 0.7171517371517371 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.66666667 0.58333333 0.58333333 0.66666667 0.81818182 0.54545455 0.72727273 0.58333333 0.66666667 0.5 ] mean value: 0.634090909090909 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.70833333 0.70833333 0.58333333 0.625 0.7826087 0.69565217 0.7826087 0.56521739 0.69565217 0.69565217] mean value: 0.6842391304347826 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.70833333 0.70833333 0.58333333 0.625 0.78409091 0.68939394 0.78030303 0.56439394 0.6969697 0.70454545] mean value: 0.684469696969697 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.53333333 0.5 0.41176471 0.47058824 0.64285714 0.46153846 0.61538462 0.41176471 0.53333333 0.46153846] mean value: 0.5042102995044171 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.42 MCC on Training: 0.38 Running classifier: 3 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.03094411 0.01701021 0.01794744 0.01665688 0.01782465 0.01630974 0.01893044 0.01713943 0.01890874 0.01983905] mean value: 0.019151067733764647 key: score_time value: [0.00936389 0.00874901 0.00879002 0.00925875 0.00906157 0.00903964 0.00871563 0.00871181 0.00879407 0.00869536] mean value: 0.008917975425720214 key: test_mcc value: [ 0.0836242 0.2508726 0.53033009 0.38490018 0.25495628 -0.13740858 0.21374669 0.31298622 0.31298622 0.39393939] mean value: 0.26009332841241306 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.52173913 0.60869565 0.78571429 0.73333333 0.66666667 0.38095238 0.57142857 0.63636364 0.63636364 0.69565217] mean value: 0.6236909467344249 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.54545455 0.63636364 0.6875 0.61111111 0.5625 0.4 0.6 0.7 0.7 0.72727273] mean value: 0.617020202020202 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 0.58333333 0.91666667 0.91666667 0.81818182 0.36363636 0.54545455 0.58333333 0.58333333 0.66666667] mean value: 0.6477272727272727 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.54166667 0.625 0.75 0.66666667 0.60869565 0.43478261 0.60869565 0.65217391 0.65217391 0.69565217] mean value: 0.623550724637681 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.54166667 0.625 0.75 0.66666667 0.61742424 0.43181818 0.60606061 0.65530303 0.65530303 0.6969697 ] mean value: 0.6246212121212122 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.35294118 0.4375 0.64705882 0.57894737 0.5 0.23529412 0.4 0.46666667 0.46666667 0.53333333] mean value: 0.4618408152734778 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.24 MCC on Training: 0.26 Running classifier: 4 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.00909209 0.00892186 0.00924301 0.00910115 0.00944424 0.00912714 0.00926042 0.00914764 0.00915337 0.00972033] mean value: 0.009221124649047851 key: score_time value: [0.00858021 0.00867033 0.00859237 0.00868607 0.00851488 0.00879908 0.00866652 0.00852203 0.00869179 0.00923824] mean value: 0.008696150779724122 key: test_mcc value: [ 0. 0.0860663 0.3380617 0.0836242 0.04545455 -0.05427825 0.02585438 0.03178209 0.01996808 0.31298622] mean value: 0.08895192652093153 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.5 0.59259259 0.69230769 0.52173913 0.52173913 0.4 0.35294118 0.59259259 0.64516129 0.63636364] mean value: 0.5455437241519248 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.5 0.53333333 0.64285714 0.54545455 0.5 0.44444444 0.5 0.53333333 0.52631579 0.7 ] mean value: 0.5425738588896484 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.5 0.66666667 0.75 0.5 0.54545455 0.36363636 0.27272727 0.66666667 0.83333333 0.58333333] mean value: 0.5681818181818181 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.5 0.54166667 0.66666667 0.54166667 0.52173913 0.47826087 0.52173913 0.52173913 0.52173913 0.65217391] mean value: 0.5467391304347826 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.5 0.54166667 0.66666667 0.54166667 0.52272727 0.47348485 0.51136364 0.51515152 0.50757576 0.65530303] mean value: 0.5435606060606061 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.33333333 0.42105263 0.52941176 0.35294118 0.35294118 0.25 0.21428571 0.42105263 0.47619048 0.46666667] mean value: 0.3817875571281144 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.29 MCC on Training: 0.09 Running classifier: 5 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.1107955 0.10783839 0.10808063 0.10998392 0.11200476 0.11127257 0.11335564 0.11341119 0.1127522 0.11296892] mean value: 0.11124637126922607 key: score_time value: [0.01802349 0.01826477 0.01833296 0.01913619 0.01896167 0.01881337 0.01831341 0.01939559 0.01976657 0.01958489] mean value: 0.018859291076660158 key: test_mcc value: [ 0.25819889 0.16666667 0. 0.3380617 -0.04545455 0.39393939 0.21374669 0.12406456 0.38932432 0.23262105] mean value: 0.20711687271217166 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.66666667 0.58333333 0.5 0.69230769 0.45454545 0.69565217 0.57142857 0.61538462 0.72 0.57142857] mean value: 0.6070747079007949 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.6 0.58333333 0.5 0.64285714 0.45454545 0.66666667 0.6 0.57142857 0.69230769 0.66666667] mean value: 0.5977805527805528 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.75 0.58333333 0.5 0.75 0.45454545 0.72727273 0.54545455 0.66666667 0.75 0.5 ] mean value: 0.6227272727272727 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.625 0.58333333 0.5 0.66666667 0.47826087 0.69565217 0.60869565 0.56521739 0.69565217 0.60869565] mean value: 0.6027173913043478 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.625 0.58333333 0.5 0.66666667 0.47727273 0.6969697 0.60606061 0.56060606 0.69318182 0.61363636] mean value: 0.6022727272727273 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.5 0.41176471 0.33333333 0.52941176 0.29411765 0.53333333 0.4 0.44444444 0.5625 0.4 ] mean value: 0.44089052287581704 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.45 MCC on Training: 0.21 Running classifier: 6 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [0.37862897 0.37261224 0.37469506 0.37925148 0.39149499 0.3821311 0.3872025 0.38922215 0.39946842 0.3783474 ] mean value: 0.3833054304122925 key: score_time value: [0.009197 0.00902843 0.009269 0.00946283 0.01010633 0.01010633 0.01008749 0.00939822 0.00897026 0.00904393] mean value: 0.009466981887817383 key: test_mcc value: [ 0.3380617 0.25819889 -0.0836242 0.16903085 0.31298622 0.30240737 0.56818182 0.13740858 0.48856385 0.5164589 ] mean value: 0.30076739861023183 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.63636364 0.57142857 0.48 0.61538462 0.66666667 0.6 0.7826087 0.54545455 0.72727273 0.7 ] mean value: 0.6325179458222937 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.7 0.66666667 0.46153846 0.57142857 0.61538462 0.66666667 0.75 0.6 0.8 0.875 ] mean value: 0.6706684981684982 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.58333333 0.5 0.5 0.66666667 0.72727273 0.54545455 0.81818182 0.5 0.66666667 0.58333333] mean value: 0.6090909090909091 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.66666667 0.625 0.45833333 0.58333333 0.65217391 0.65217391 0.7826087 0.56521739 0.73913043 0.73913043] mean value: 0.6463768115942028 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.66666667 0.625 0.45833333 0.58333333 0.65530303 0.64772727 0.78409091 0.56818182 0.74242424 0.74621212] mean value: 0.6477272727272727 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.46666667 0.4 0.31578947 0.44444444 0.5 0.42857143 0.64285714 0.375 0.57142857 0.53846154] mean value: 0.4683219266114002 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.34 MCC on Training: 0.3 Running classifier: 7 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.01022983 0.01024127 0.01030922 0.00971866 0.01016998 0.00950384 0.01032901 0.00936103 0.00944877 0.01048279] mean value: 0.009979438781738282 key: score_time value: [0.00947666 0.00888681 0.00986218 0.00901818 0.00971508 0.00960755 0.0091567 0.00991869 0.00949574 0.00998235] mean value: 0.009511995315551757 key: test_mcc value: [0.41812101 0.43033148 0.0836242 0.33333333 0.39727608 0.3030303 0.39393939 0.05427825 0.5164589 0.31298622] mean value: 0.3243379164669292 key: train_mcc value: [0.40302748 0.42768869 0.45883147 0.44892394 0.4265046 0.40287511 0.40736541 0.49105457 0.42469058 0.3933675 ] mean value: 0.4284329362241775 key: test_fscore value: [0.69565217 0.66666667 0.52173913 0.66666667 0.63157895 0.63636364 0.69565217 0.47619048 0.7 0.63636364] mean value: 0.6326873507880373 key: train_fscore value: [0.65217391 0.67724868 0.71641791 0.71287129 0.69035533 0.7014218 0.68020305 0.72727273 0.69035533 0.69230769] mean value: 0.6940627713980673 key: test_precision value: [0.72727273 0.77777778 0.54545455 0.66666667 0.75 0.63636364 0.66666667 0.55555556 0.875 0.7 ] mean value: 0.6900757575757577 key: train_precision value: [0.75949367 0.76190476 0.75 0.74226804 0.74725275 0.7047619 0.73626374 0.77419355 0.73913043 0.69902913] mean value: 0.7414297971689637 key: test_recall value: [0.66666667 0.58333333 0.5 0.66666667 0.54545455 0.63636364 0.72727273 0.41666667 0.58333333 0.58333333] mean value: 0.5909090909090908 key: train_recall value: [0.57142857 0.60952381 0.68571429 0.68571429 0.64150943 0.69811321 0.63207547 0.68571429 0.64761905 0.68571429] mean value: 0.6543126684636119 key: test_accuracy value: [0.70833333 0.70833333 0.54166667 0.66666667 0.69565217 0.65217391 0.69565217 0.52173913 0.73913043 0.65217391] mean value: 0.6581521739130435 key: train_accuracy value: [0.6952381 0.70952381 0.72857143 0.72380952 0.71090047 0.7014218 0.7014218 0.74407583 0.71090047 0.69668246] mean value: 0.7122545700744751 key: test_roc_auc value: [0.70833333 0.70833333 0.54166667 0.66666667 0.68939394 0.65151515 0.6969697 0.52651515 0.74621212 0.65530303] mean value: 0.6590909090909091 key: train_roc_auc value: [0.6952381 0.70952381 0.72857143 0.72380952 0.71123091 0.70143756 0.70175202 0.74380054 0.71060198 0.69663073] mean value: 0.7122596585804132 key: test_jcc value: [0.53333333 0.5 0.35294118 0.5 0.46153846 0.46666667 0.53333333 0.3125 0.53846154 0.46666667] mean value: 0.4665441176470589 key: train_jcc value: [0.48387097 0.512 0.55813953 0.55384615 0.52713178 0.54014599 0.51538462 0.57142857 0.52713178 0.52941176] mean value: 0.5318491159283811 MCC on Blind test: 0.3 MCC on Training: 0.32 Running classifier: 8 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.17530155 0.07387185 0.06599808 0.0392282 0.08214688 0.03195858 0.08130693 0.07256031 0.06431103 0.03117085] mean value: 0.07178542613983155 key: score_time value: [0.0394702 0.02776504 0.01399827 0.02727485 0.02855206 0.02705956 0.02276826 0.02356625 0.02539086 0.0140655 ] mean value: 0.0249910831451416 key: test_mcc value: [ 0.16666667 0.0860663 -0.0836242 0.2508726 -0.17236256 0.04545455 0.02585438 -0.25495628 0.3030303 0.31298622] mean value: 0.06799879715896232 key: train_mcc value: [0.96190476 0.98113038 0.95276992 0.9620793 1. 0.94330129 0.96226076 0.98104223 0.97195268 0.97160572] mean value: 0.9688047040068628 key: test_fscore value: [0.58333333 0.47619048 0.43478261 0.60869565 0.23529412 0.52173913 0.35294118 0.5 0.66666667 0.63636364] mean value: 0.5016006797976108 key: train_fscore value: [0.98095238 0.99038462 0.97584541 0.98076923 1. 0.97142857 0.98095238 0.99047619 0.98550725 0.98564593] mean value: 0.9841961959982555 key: test_precision value: [0.58333333 0.55555556 0.45454545 0.63636364 0.33333333 0.5 0.5 0.4375 0.66666667 0.7 ] mean value: 0.536729797979798 key: train_precision value: [0.98095238 1. 0.99019608 0.99029126 1. 0.98076923 0.99038462 0.99047619 1. 0.99038462] mean value: 0.9913454373534327 key: test_recall value: [0.58333333 0.41666667 0.41666667 0.58333333 0.18181818 0.54545455 0.27272727 0.58333333 0.66666667 0.58333333] mean value: 0.4833333333333333 key: train_recall value: [0.98095238 0.98095238 0.96190476 0.97142857 1. 0.96226415 0.97169811 0.99047619 0.97142857 0.98095238] mean value: 0.9772057502246181 key: test_accuracy value: [0.58333333 0.54166667 0.45833333 0.625 0.43478261 0.52173913 0.52173913 0.39130435 0.65217391 0.65217391] mean value: 0.5382246376811595 key: train_accuracy value: [0.98095238 0.99047619 0.97619048 0.98095238 1. 0.97156398 0.98104265 0.99052133 0.98578199 0.98578199] mean value: 0.984326337169939 key: test_roc_auc value: [0.58333333 0.54166667 0.45833333 0.625 0.42424242 0.52272727 0.51136364 0.38257576 0.65151515 0.65530303] mean value: 0.5356060606060605 key: train_roc_auc value: [0.98095238 0.99047619 0.97619048 0.98095238 1. 0.97160827 0.98108715 0.99052111 0.98571429 0.98575921] mean value: 0.9843261455525607 key: test_jcc value: [0.41176471 0.3125 0.27777778 0.4375 0.13333333 0.35294118 0.21428571 0.33333333 0.5 0.46666667] mean value: 0.3440102707749767 key: train_jcc value: [0.96261682 0.98095238 0.95283019 0.96226415 1. 0.94444444 0.96261682 0.98113208 0.97142857 0.97169811] mean value: 0.9689983569987095 MCC on Blind test: 0.34 MCC on Training: 0.07 Running classifier: 9 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.02997422 0.0100069 0.0094707 0.00933766 0.00944424 0.00969315 0.00984168 0.00980806 0.00954342 0.00922322] mean value: 0.011634325981140137 key: score_time value: [0.01787376 0.01075125 0.01080418 0.01119447 0.01090479 0.01063418 0.01115108 0.01079726 0.0107336 0.01088953] mean value: 0.011573410034179688 key: test_mcc value: [ 0.25819889 0.1767767 -0.16903085 0.33333333 -0.05427825 0.31298622 0.03178209 -0.3030303 0.21969697 -0.03816905] mean value: 0.07682657422483104 key: train_mcc value: [0.54443968 0.5048535 0.50512857 0.48713024 0.52220055 0.48973314 0.48814016 0.55849348 0.48617936 0.50917106] mean value: 0.5095469734493033 key: test_fscore value: [0.57142857 0.5 0.46153846 0.66666667 0.4 0.66666667 0.42105263 0.34782609 0.60869565 0.45454545] mean value: 0.5098420191555203 key: train_fscore value: [0.76237624 0.75 0.74757282 0.73267327 0.74111675 0.73529412 0.74528302 0.76142132 0.71204188 0.74 ] mean value: 0.7427779412882203 key: test_precision value: [0.66666667 0.625 0.42857143 0.66666667 0.44444444 0.61538462 0.5 0.36363636 0.63636364 0.5 ] mean value: 0.5446733821733822 key: train_precision value: [0.79381443 0.75728155 0.76237624 0.7628866 0.8021978 0.76530612 0.74528302 0.81521739 0.79069767 0.77894737] mean value: 0.7774008199608367 key: test_recall value: [0.5 0.41666667 0.5 0.66666667 0.36363636 0.72727273 0.36363636 0.33333333 0.58333333 0.41666667] mean value: 0.4871212121212121 key: train_recall value: [0.73333333 0.74285714 0.73333333 0.7047619 0.68867925 0.70754717 0.74528302 0.71428571 0.64761905 0.7047619 ] mean value: 0.7122461814914646 key: test_accuracy value: [0.625 0.58333333 0.41666667 0.66666667 0.47826087 0.65217391 0.52173913 0.34782609 0.60869565 0.47826087] mean value: 0.5378623188405798 key: train_accuracy value: [0.77142857 0.75238095 0.75238095 0.74285714 0.75829384 0.74407583 0.74407583 0.77725118 0.73933649 0.7535545 ] mean value: 0.7535635296772737 key: test_roc_auc value: [0.625 0.58333333 0.41666667 0.66666667 0.47348485 0.65530303 0.51515152 0.34848485 0.60984848 0.48106061] mean value: 0.5375000000000001 key: train_roc_auc value: [0.77142857 0.75238095 0.75238095 0.74285714 0.75862534 0.74424978 0.74407008 0.77695418 0.73890386 0.75332435] mean value: 0.7535175202156335 key: test_jcc value: [0.4 0.33333333 0.3 0.5 0.25 0.5 0.26666667 0.21052632 0.4375 0.29411765] mean value: 0.3492143962848297 key: train_jcc value: [0.616 0.6 0.59689922 0.578125 0.58870968 0.58139535 0.59398496 0.6147541 0.55284553 0.58730159] mean value: 0.5910015427586307 MCC on Blind test: 0.25 MCC on Training: 0.08 Running classifier: 10 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.03327894 0.07228994 0.06189537 0.06273246 0.06427145 0.06275225 0.07481647 0.04446435 0.0606854 0.08739185] mean value: 0.062457847595214847 key: score_time value: [0.02104044 0.02423787 0.0238874 0.02019048 0.02095771 0.02159691 0.02962255 0.01925206 0.01991701 0.0312376 ] mean value: 0.023194003105163574 key: test_mcc value: [-0.0860663 0. 0. 0. -0.23262105 0.05427825 0.02585438 -0.23262105 0.31298622 0.15096491] mean value: -0.000722464322827332 key: train_mcc value: [0.76193932 0.76221593 0.70504974 0.73386604 0.81994609 0.75413531 0.76336841 0.82020868 0.7357111 0.67781076] mean value: 0.7534251375337022 key: test_fscore value: [0.51851852 0.5 0.57142857 0.57142857 0.3 0.56 0.35294118 0.46153846 0.63636364 0.5 ] mean value: 0.4972218935748348 key: train_fscore value: [0.88151659 0.87922705 0.85024155 0.86407767 0.90995261 0.875 0.88038278 0.90821256 0.8627451 0.83653846] mean value: 0.8747894358333292 key: test_precision value: [0.46666667 0.5 0.5 0.5 0.33333333 0.5 0.5 0.42857143 0.7 0.625 ] mean value: 0.5053571428571428 key: train_precision value: [0.87735849 0.89215686 0.8627451 0.88118812 0.91428571 0.89215686 0.89320388 0.92156863 0.88888889 0.84466019] mean value: 0.8868212741202817 key: test_recall value: [0.58333333 0.5 0.66666667 0.66666667 0.27272727 0.63636364 0.27272727 0.5 0.58333333 0.41666667] mean value: 0.5098484848484849 key: train_recall value: [0.88571429 0.86666667 0.83809524 0.84761905 0.90566038 0.85849057 0.86792453 0.8952381 0.83809524 0.82857143] mean value: 0.8632075471698114 key: test_accuracy value: [0.45833333 0.5 0.5 0.5 0.39130435 0.52173913 0.52173913 0.39130435 0.65217391 0.56521739] mean value: 0.5001811594202898 key: train_accuracy value: [0.88095238 0.88095238 0.85238095 0.86666667 0.90995261 0.87677725 0.88151659 0.90995261 0.86729858 0.83886256] mean value: 0.8765312570525842 key: test_roc_auc value: [0.45833333 0.5 0.5 0.5 0.38636364 0.52651515 0.51136364 0.38636364 0.65530303 0.5719697 ] mean value: 0.4996212121212122 key: train_roc_auc value: [0.88095238 0.88095238 0.85238095 0.86666667 0.90997305 0.87686433 0.88158131 0.9098832 0.86716083 0.83881402] mean value: 0.8765229110512129 key: test_jcc value: [0.35 0.33333333 0.4 0.4 0.17647059 0.38888889 0.21428571 0.3 0.46666667 0.33333333] mean value: 0.3362978524743231 key: train_jcc value: [0.78813559 0.78448276 0.7394958 0.76068376 0.83478261 0.77777778 0.78632479 0.83185841 0.75862069 0.71900826] mean value: 0.7781170444839962 MCC on Blind test: 0.14 MCC on Training: -0.0 Running classifier: 11 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.04258418 0.034127 0.03429818 0.03415656 0.03370333 0.03523183 0.0342195 0.0351088 0.0342834 0.03301573] mean value: 0.035072851181030276 key: score_time value: [0.01188707 0.01186633 0.01185369 0.0118885 0.01191688 0.01192927 0.01192331 0.01182652 0.01190424 0.01186228] mean value: 0.011885809898376464 key: test_mcc value: [0.45834925 0.3380617 0. 0.2508726 0.31298622 0.12878788 0.12406456 0.03816905 0.33371191 0.21969697] mean value: 0.2204700139032572 key: train_mcc value: [0.60977275 0.63855876 0.6194973 0.56231294 0.62135931 0.60239131 0.62135931 0.61259427 0.59241621 0.55477669] mean value: 0.6035038865492163 key: test_fscore value: [0.75862069 0.63636364 0.5 0.64 0.66666667 0.54545455 0.5 0.56 0.6 0.60869565] mean value: 0.6015801190313934 key: train_fscore value: [0.80193237 0.81553398 0.80582524 0.77669903 0.80769231 0.79807692 0.80769231 0.79802956 0.79425837 0.77934272] mean value: 0.7985082810899164 key: test_precision value: [0.64705882 0.7 0.5 0.61538462 0.61538462 0.54545455 0.55555556 0.53846154 0.75 0.63636364] mean value: 0.6103663330133919 key: train_precision value: [0.81372549 0.83168317 0.82178218 0.79207921 0.82352941 0.81372549 0.82352941 0.82653061 0.79807692 0.76851852] mean value: 0.8113180412217356 key: test_recall value: [0.91666667 0.58333333 0.5 0.66666667 0.72727273 0.54545455 0.45454545 0.58333333 0.5 0.58333333] mean value: 0.606060606060606 key: train_recall value: [0.79047619 0.8 0.79047619 0.76190476 0.79245283 0.78301887 0.79245283 0.77142857 0.79047619 0.79047619] mean value: 0.7863162623539981 key: test_accuracy value: [0.70833333 0.66666667 0.5 0.625 0.65217391 0.56521739 0.56521739 0.52173913 0.65217391 0.60869565] mean value: 0.6065217391304348 key: train_accuracy value: [0.8047619 0.81904762 0.80952381 0.78095238 0.81042654 0.80094787 0.81042654 0.8056872 0.79620853 0.77725118] mean value: 0.8015233581584292 key: test_roc_auc value: [0.70833333 0.66666667 0.5 0.625 0.65530303 0.56439394 0.56060606 0.51893939 0.65909091 0.60984848] mean value: 0.6068181818181818 key: train_roc_auc value: [0.8047619 0.81904762 0.80952381 0.78095238 0.81051213 0.80103324 0.81051213 0.80552561 0.79618149 0.77731357] mean value: 0.8015363881401617 key: test_jcc value: [0.61111111 0.46666667 0.33333333 0.47058824 0.5 0.375 0.33333333 0.38888889 0.42857143 0.4375 ] mean value: 0.43449929971988793 key: train_jcc value: [0.66935484 0.68852459 0.67479675 0.63492063 0.67741935 0.664 0.67741935 0.66393443 0.65873016 0.63846154] mean value: 0.6647561644860351 MCC on Blind test: 0.38 MCC on Training: 0.22 Running classifier: 12 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(cv=3, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(cv=3, random_state=42))]) key: fit_time value: [0.57566547 0.4727242 0.4690578 0.47469425 0.46609449 0.60962796 0.54123306 0.51206398 0.51154375 0.58188534] mean value: 0.5214590311050415 key: score_time value: [0.01269269 0.01266479 0.0121274 0.01218367 0.01226377 0.01312423 0.01324177 0.01333547 0.01339674 0.01366973] mean value: 0.012870025634765626 key: test_mcc value: [0.45834925 0.41812101 0. 0.2508726 0.31298622 0.30240737 0.3030303 0.03816905 0.25495628 0.13740858] mean value: 0.2476300662968462 key: train_mcc value: [0.53335752 0.42927286 0.53335752 0.50512857 0.52605571 0.51662174 0.43129327 0.56476831 0.52619283 0.50755131] mean value: 0.5073599646783141 key: test_fscore value: [0.75862069 0.69565217 0.5 0.60869565 0.66666667 0.6 0.63636364 0.56 0.52631579 0.54545455] mean value: 0.6097769153700661 key: train_fscore value: [0.76555024 0.70588235 0.76777251 0.75700935 0.76415094 0.75829384 0.71962617 0.7745098 0.76415094 0.75700935] mean value: 0.7533955493413632 key: test_precision value: [0.64705882 0.72727273 0.5 0.63636364 0.61538462 0.66666667 0.63636364 0.53846154 0.71428571 0.6 ] mean value: 0.6281857358327947 key: train_precision value: [0.76923077 0.72727273 0.76415094 0.74311927 0.76415094 0.76190476 0.71296296 0.7979798 0.75700935 0.74311927] mean value: 0.7540900784047956 key: test_recall value: [0.91666667 0.66666667 0.5 0.58333333 0.72727273 0.54545455 0.63636364 0.58333333 0.41666667 0.5 ] mean value: 0.6075757575757577 key: train_recall value: [0.76190476 0.68571429 0.77142857 0.77142857 0.76415094 0.75471698 0.72641509 0.75238095 0.77142857 0.77142857] mean value: 0.753099730458221 key: test_accuracy value: [0.70833333 0.70833333 0.5 0.625 0.65217391 0.65217391 0.65217391 0.52173913 0.60869565 0.56521739] mean value: 0.6193840579710146 key: train_accuracy value: [0.76666667 0.71428571 0.76666667 0.75238095 0.76303318 0.75829384 0.71563981 0.78199052 0.76303318 0.7535545 ] mean value: 0.7535545023696681 key: test_roc_auc value: [0.70833333 0.70833333 0.5 0.625 0.65530303 0.64772727 0.65151515 0.51893939 0.61742424 0.56818182] mean value: 0.6200757575757576 key: train_roc_auc value: [0.76666667 0.71428571 0.76666667 0.75238095 0.76302785 0.75831087 0.7155885 0.78185085 0.76307278 0.75363881] mean value: 0.753548966756514 key: test_jcc value: [0.61111111 0.53333333 0.33333333 0.4375 0.5 0.42857143 0.46666667 0.38888889 0.35714286 0.375 ] mean value: 0.4431547619047619 key: train_jcc value: [0.62015504 0.54545455 0.62307692 0.60902256 0.61832061 0.61068702 0.5620438 0.632 0.61832061 0.60902256] mean value: 0.604810365996836 MCC on Blind test: 0.37 MCC on Training: 0.25 Running classifier: 13 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [0.79620123 0.35570121 0.75309753 1.10130954 0.45094538 0.78829217 0.42300057 0.96657062 0.37601089 0.23680067] mean value: 0.6247929811477662 key: score_time value: [0.01523924 0.01227593 0.01220226 0.01361418 0.01215959 0.01215672 0.01214361 0.01224566 0.01221895 0.01226735] mean value: 0.012652349472045899 key: test_mcc value: [ 0.33333333 0.19245009 -0.0836242 0.2508726 0.33371191 0.30240737 0.12878788 0.12406456 0.3030303 0.33371191] mean value: 0.22187457491206888 key: train_mcc value: [0.59808917 0.5123464 0.65264046 0.70556232 0.48351089 0.68092577 0.52026408 0.64754073 0.5501591 0.45980998] mean value: 0.5810848903547738 key: test_fscore value: [0.66666667 0.44444444 0.48 0.64 0.69230769 0.6 0.54545455 0.61538462 0.66666667 0.6 ] mean value: 0.5950924630924631 key: train_fscore value: [0.74860335 0.72916667 0.83408072 0.84878049 0.76470588 0.83168317 0.77333333 0.83185841 0.78571429 0.69148936] mean value: 0.7839415662414806 key: test_precision value: [0.66666667 0.66666667 0.46153846 0.61538462 0.6 0.66666667 0.54545455 0.57142857 0.66666667 0.75 ] mean value: 0.6210472860472861 key: train_precision value: [0.90540541 0.8045977 0.78813559 0.87 0.68939394 0.875 0.73109244 0.7768595 0.7394958 0.78313253] mean value: 0.7963112908715939 key: test_recall value: [0.66666667 0.33333333 0.5 0.66666667 0.81818182 0.54545455 0.54545455 0.66666667 0.66666667 0.5 ] mean value: 0.5909090909090909 key: train_recall value: [0.63809524 0.66666667 0.88571429 0.82857143 0.85849057 0.79245283 0.82075472 0.8952381 0.83809524 0.61904762] mean value: 0.7843126684636118 key: test_accuracy value: [0.66666667 0.58333333 0.45833333 0.625 0.65217391 0.65217391 0.56521739 0.56521739 0.65217391 0.65217391] mean value: 0.6072463768115942 key: train_accuracy value: [0.78571429 0.75238095 0.82380952 0.85238095 0.73459716 0.83886256 0.75829384 0.81990521 0.77251185 0.72511848] mean value: 0.786357481381178 key: test_roc_auc value: [0.66666667 0.58333333 0.45833333 0.625 0.65909091 0.64772727 0.56439394 0.56060606 0.65151515 0.65909091] mean value: 0.6075757575757575 key: train_roc_auc value: [0.78571429 0.75238095 0.82380952 0.85238095 0.73400719 0.83908356 0.75799641 0.82026056 0.7728212 0.72461815] mean value: 0.7863072776280324 key: test_jcc value: [0.5 0.28571429 0.31578947 0.47058824 0.52941176 0.42857143 0.375 0.44444444 0.5 0.42857143] mean value: 0.42780910609857975 key: train_jcc value: [0.59821429 0.57377049 0.71538462 0.73728814 0.61904762 0.71186441 0.63043478 0.71212121 0.64705882 0.52845528] mean value: 0.6473639657134844 MCC on Blind test: 0.25 MCC on Training: 0.22 Running classifier: 14 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.01322222 0.0127933 0.00931907 0.00937986 0.00969148 0.0099597 0.00981092 0.01001048 0.01051688 0.00922322] mean value: 0.010392713546752929 key: score_time value: [0.01176333 0.01014638 0.00869036 0.00966525 0.00925994 0.00953317 0.00953579 0.0086441 0.00886631 0.0090661 ] mean value: 0.009517073631286621 key: test_mcc value: [ 0.60246408 0.41812101 0.0836242 0.2508726 0.41096386 0.05427825 0.31298622 -0.12878788 0.3030303 0.04545455] mean value: 0.23530071784624312 key: train_mcc value: [0.33387889 0.39111527 0.42888275 0.33387889 0.37440462 0.30807268 0.36518252 0.39470491 0.36546777 0.31787132] mean value: 0.36134596079366116 key: test_fscore value: [0.81481481 0.69565217 0.56 0.64 0.72 0.56 0.66666667 0.43478261 0.66666667 0.52173913] mean value: 0.6280322061191626 key: train_fscore value: [0.67592593 0.68627451 0.71962617 0.67592593 0.69158879 0.66046512 0.69124424 0.70642202 0.68837209 0.6635514 ] mean value: 0.6859396184078246 key: test_precision value: [0.73333333 0.72727273 0.53846154 0.61538462 0.64285714 0.5 0.61538462 0.45454545 0.66666667 0.54545455] mean value: 0.6039360639360639 key: train_precision value: [0.65765766 0.70707071 0.70642202 0.65765766 0.68518519 0.65137615 0.67567568 0.68141593 0.67272727 0.65137615] mean value: 0.6746564397104302 key: test_recall value: [0.91666667 0.66666667 0.58333333 0.66666667 0.81818182 0.63636364 0.72727273 0.41666667 0.66666667 0.5 ] mean value: 0.6598484848484849 key: train_recall value: [0.6952381 0.66666667 0.73333333 0.6952381 0.69811321 0.66981132 0.70754717 0.73333333 0.7047619 0.67619048] mean value: 0.6980233602875112 key: test_accuracy value: [0.79166667 0.70833333 0.54166667 0.625 0.69565217 0.52173913 0.65217391 0.43478261 0.65217391 0.52173913] mean value: 0.6144927536231883 key: train_accuracy value: [0.66666667 0.6952381 0.71428571 0.66666667 0.68720379 0.65402844 0.68246445 0.69668246 0.68246445 0.65876777] mean value: 0.6804468517264726 key: test_roc_auc value: [0.79166667 0.70833333 0.54166667 0.625 0.70075758 0.52651515 0.65530303 0.43560606 0.65151515 0.52272727] mean value: 0.6159090909090909 key: train_roc_auc value: [0.66666667 0.6952381 0.71428571 0.66666667 0.68715184 0.65395328 0.68234501 0.69685535 0.68256963 0.65884996] mean value: 0.6804582210242588 key: test_jcc value: [0.6875 0.53333333 0.38888889 0.47058824 0.5625 0.38888889 0.5 0.27777778 0.5 0.35294118] mean value: 0.4662418300653594 key: train_jcc value: [0.51048951 0.52238806 0.5620438 0.51048951 0.52857143 0.49305556 0.52816901 0.54609929 0.5248227 0.4965035 ] mean value: 0.5222632356831541 MCC on Blind test: 0.25 MCC on Training: 0.24 Running classifier: 15 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.0128355 0.01022625 0.01034784 0.01026058 0.01021051 0.00911212 0.01055455 0.01075006 0.01096702 0.01099372] mean value: 0.010625815391540528 key: score_time value: [0.00945282 0.00944543 0.00956273 0.00974488 0.00980544 0.00884271 0.00931215 0.00985527 0.00976229 0.00986481] mean value: 0.009564852714538575 key: test_mcc value: [ 0. 0.0836242 0.16903085 0.2236068 0.12878788 -0.03816905 0.12844577 -0.03816905 0.06579517 0.05427825] mean value: 0.07772308141158425 key: train_mcc value: [0.35555562 0.28867513 0.42059551 0.3699815 0.36908703 0.4520146 0.39969265 0.40771019 0.44380528 0.41599376] mean value: 0.3923111284043744 key: test_fscore value: [0.4 0.56 0.54545455 0.375 0.54545455 0.5 0.375 0.45454545 0.42105263 0.47619048] mean value: 0.4652697653223969 key: train_fscore value: [0.65306122 0.61538462 0.69651741 0.61797753 0.65989848 0.73873874 0.67010309 0.67357513 0.70050761 0.6557377 ] mean value: 0.6681501538244136 key: test_precision value: [0.5 0.53846154 0.6 0.75 0.54545455 0.46153846 0.6 0.5 0.57142857 0.55555556] mean value: 0.5622438672438672 key: train_precision value: [0.7032967 0.66666667 0.72916667 0.75342466 0.71428571 0.70689655 0.73863636 0.73863636 0.75 0.76923077] mean value: 0.7270240456677632 key: test_recall value: [0.33333333 0.58333333 0.5 0.25 0.54545455 0.54545455 0.27272727 0.41666667 0.33333333 0.41666667] mean value: 0.41969696969696973 key: train_recall value: [0.60952381 0.57142857 0.66666667 0.52380952 0.61320755 0.77358491 0.61320755 0.61904762 0.65714286 0.57142857] mean value: 0.621904761904762 key: test_accuracy value: [0.5 0.54166667 0.58333333 0.58333333 0.56521739 0.47826087 0.56521739 0.47826087 0.52173913 0.52173913] mean value: 0.5338768115942027 key: train_accuracy value: [0.67619048 0.64285714 0.70952381 0.67619048 0.68246445 0.72511848 0.69668246 0.7014218 0.72037915 0.7014218 ] mean value: 0.6932250056420672 key: test_roc_auc value: [0.5 0.54166667 0.58333333 0.58333333 0.56439394 0.48106061 0.5530303 0.48106061 0.53030303 0.52651515] mean value: 0.5344696969696969 key: train_roc_auc value: [0.67619048 0.64285714 0.70952381 0.67619048 0.68279425 0.72488769 0.69707996 0.70103324 0.72008086 0.70080863] mean value: 0.6931446540880504 key: test_jcc value: [0.25 0.38888889 0.375 0.23076923 0.375 0.33333333 0.23076923 0.29411765 0.26666667 0.3125 ] mean value: 0.3057044997486174 key: train_jcc value: [0.48484848 0.44444444 0.53435115 0.44715447 0.49242424 0.58571429 0.50387597 0.5078125 0.5390625 0.48780488] mean value: 0.502749292105537 MCC on Blind test: 0.15 MCC on Training: 0.08 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=12, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.01251221 0.01435089 0.0146358 0.01553273 0.01512647 0.01604581 0.01613927 0.01594496 0.03357387 0.01685143] mean value: 0.017071342468261717 key: score_time value: [0.00986791 0.01193047 0.0118711 0.01207519 0.01179147 0.01191831 0.01189876 0.01201749 0.01223636 0.01433158] mean value: 0.011993861198425293 key: test_mcc value: [ 0. -0.1767767 0.0860663 0.20851441 0.31252706 0.11236664 0.01996808 -0.06579517 0.12844577 0.11236664] mean value: 0.07376830376343328 key: train_mcc value: [0.11433239 0.4793566 0.50756473 0.16458064 0.44318642 0.43772687 0.47959331 0.59642721 0.34811818 0.38880022] mean value: 0.3959686583900703 key: test_fscore value: [0.66666667 0.5 0.59259259 0.68571429 0.55555556 0.64516129 0.26666667 0.57142857 0.66666667 0.26666667] mean value: 0.5417118962280252 key: train_fscore value: [0.67313916 0.76068376 0.77253219 0.67973856 0.57692308 0.75182482 0.57894737 0.81034483 0.71672355 0.46043165] mean value: 0.6781288964805415 key: test_precision value: [0.5 0.4375 0.53333333 0.52173913 0.71428571 0.5 0.5 0.5 0.55555556 0.66666667] mean value: 0.5429080400276052 key: train_precision value: [0.50980392 0.68992248 0.703125 0.51741294 0.9 0.61309524 0.95652174 0.74015748 0.55851064 0.94117647] mean value: 0.7129725903938906 key: test_recall value: [1. 0.58333333 0.66666667 1. 0.45454545 0.90909091 0.18181818 0.66666667 0.83333333 0.16666667] mean value: 0.6462121212121212 key: train_recall value: [0.99047619 0.84761905 0.85714286 0.99047619 0.4245283 0.97169811 0.41509434 0.8952381 1. 0.3047619 ] mean value: 0.7697035040431267 key: test_accuracy value: [0.5 0.41666667 0.54166667 0.54166667 0.65217391 0.52173913 0.52173913 0.47826087 0.56521739 0.52173913] mean value: 0.5260869565217391 key: train_accuracy value: [0.51904762 0.73333333 0.74761905 0.53333333 0.68720379 0.67772512 0.69668246 0.79146919 0.60663507 0.64454976] mean value: 0.6637598736176935 key: test_roc_auc value: [0.5 0.41666667 0.54166667 0.54166667 0.64393939 0.53787879 0.50757576 0.46969697 0.5530303 0.53787879] mean value: 0.525 key: train_roc_auc value: [0.51904762 0.73333333 0.74761905 0.53333333 0.68845463 0.67632525 0.69802336 0.79195867 0.60849057 0.64294699] mean value: 0.6639532794249774 key: test_jcc value: [0.5 0.33333333 0.42105263 0.52173913 0.38461538 0.47619048 0.15384615 0.4 0.5 0.15384615] mean value: 0.38446232638452316 key: train_jcc value: [0.50731707 0.6137931 0.62937063 0.51485149 0.40540541 0.60233918 0.40740741 0.68115942 0.55851064 0.29906542] mean value: 0.521921976438599 MCC on Blind test: 0.1 MCC on Training: 0.07 Running classifier: 17 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.02254272 0.02146435 0.02137876 0.02169919 0.02251363 0.02251506 0.02129984 0.02251649 0.02205539 0.02287579] mean value: 0.022086119651794432 key: score_time value: [0.01311183 0.01250124 0.01281905 0.01278996 0.0125978 0.01250792 0.01239562 0.01231647 0.01271319 0.0122745 ] mean value: 0.012602758407592774 key: test_mcc value: [ 0.0860663 -0.2508726 0.0860663 0.19245009 0.03816905 -0.17236256 -0.03178209 -0.04545455 -0.02585438 -0.12406456] mean value: -0.024763901005356775 key: train_mcc value: [1. 1. 1. 1. 1. 1. 0.9812169 1. 1. 1. ] mean value: 0.9981216895553644 key: test_fscore value: [0.59259259 0.4 0.59259259 0.66666667 0.47619048 0.23529412 0.53846154 0.5 0.33333333 0.38095238] mean value: 0.47160836984366405 key: train_fscore value: [1. 1. 1. 1. 1. 1. 0.99065421 1. 1. 1. ] mean value: 0.9990654205607477 key: test_precision value: [0.53333333 0.38461538 0.53333333 0.55555556 0.5 0.33333333 0.46666667 0.5 0.5 0.44444444] mean value: 0.47512820512820514 key: train_precision value: [1. 1. 1. 1. 1. 1. 0.98148148 1. 1. 1. ] mean value: 0.9981481481481481 key: test_recall value: [0.66666667 0.41666667 0.66666667 0.83333333 0.45454545 0.18181818 0.63636364 0.5 0.25 0.33333333] mean value: 0.49393939393939384 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.54166667 0.375 0.54166667 0.58333333 0.52173913 0.43478261 0.47826087 0.47826087 0.47826087 0.43478261] mean value: 0.4867753623188406 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 0.99052133 1. 1. 1. ] mean value: 0.9990521327014218 key: test_roc_auc value: [0.54166667 0.375 0.54166667 0.58333333 0.51893939 0.42424242 0.48484848 0.47727273 0.48863636 0.43939394] mean value: 0.4875 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 0.99047619 1. 1. 1. ] mean value: 0.9990476190476191 key: test_jcc value: [0.42105263 0.25 0.42105263 0.5 0.3125 0.13333333 0.36842105 0.33333333 0.2 0.23529412] mean value: 0.31749871001031993 key: train_jcc value: [1. 1. 1. 1. 1. 1. 0.98148148 1. 1. 1. ] mean value: 0.9981481481481481 MCC on Blind test: 0.28 MCC on Training: -0.02 Running classifier: 18 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42))]) key: fit_time value: [0.61931133 0.65735674 0.65264106 0.63982892 0.63503432 0.6507349 0.61431193 0.62149405 0.61273623 0.61791301] mean value: 0.6321362495422364 key: score_time value: [0.19718528 0.18174458 0.18775797 0.13461328 0.16591215 0.14895558 0.17596292 0.16672683 0.16879654 0.13921237] mean value: 0.166686749458313 key: test_mcc value: [ 0.2508726 0.50709255 -0.0860663 0.41812101 0.04545455 0.65151515 0.21374669 0.30240737 0.58930667 0.31298622] mean value: 0.320543650047179 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.64 0.72727273 0.51851852 0.72 0.52173913 0.81818182 0.57142857 0.69230769 0.76190476 0.63636364] mean value: 0.6607716856412508 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.61538462 0.8 0.46666667 0.69230769 0.5 0.81818182 0.6 0.64285714 0.88888889 0.7 ] mean value: 0.6724286824286824 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.66666667 0.66666667 0.58333333 0.75 0.54545455 0.81818182 0.54545455 0.75 0.66666667 0.58333333] mean value: 0.6575757575757576 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.625 0.75 0.45833333 0.70833333 0.52173913 0.82608696 0.60869565 0.65217391 0.7826087 0.65217391] mean value: 0.6585144927536233 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.625 0.75 0.45833333 0.70833333 0.52272727 0.82575758 0.60606061 0.64772727 0.78787879 0.65530303] mean value: 0.6587121212121213 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.47058824 0.57142857 0.35 0.5625 0.35294118 0.69230769 0.4 0.52941176 0.61538462 0.46666667] mean value: 0.5011228722258134 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.38 MCC on Training: 0.32 Running classifier: 19 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea...age_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42))]) key: fit_time value: [0.94843435 0.96499968 0.92683768 1.01235414 0.93469501 0.96815228 0.96667981 0.96211433 0.96982956 0.97865486] mean value: 0.9632751703262329 key: score_time value: [0.17919946 0.21667695 0.17779803 0.17387295 0.18636537 0.20376921 0.23840427 0.16899753 0.21280599 0.24649858] mean value: 0.20043883323669434 key: test_mcc value: [0.25819889 0.50709255 0. 0.5 0.41096386 0.38932432 0.3030303 0.03178209 0.41096386 0.41096386] mean value: 0.3222319731252927 key: train_mcc value: [0.84823477 0.86666667 0.87623022 0.87654832 0.90521114 0.87714571 0.87714571 0.88640248 0.92416891 0.86789603] mean value: 0.8805649968527556 key: test_fscore value: [0.66666667 0.72727273 0.53846154 0.75 0.72 0.66666667 0.63636364 0.59259259 0.66666667 0.66666667] mean value: 0.6631357161357162 key: train_fscore value: [0.9223301 0.93333333 0.93779904 0.93719807 0.95283019 0.93779904 0.93779904 0.94230769 0.96190476 0.93203883] mean value: 0.9395340105083321 key: test_precision value: [0.6 0.8 0.5 0.75 0.64285714 0.7 0.63636364 0.53333333 0.77777778 0.77777778] mean value: 0.6718109668109667 key: train_precision value: [0.94059406 0.93333333 0.94230769 0.95098039 0.95283019 0.95145631 0.95145631 0.95145631 0.96190476 0.95049505] mean value: 0.9486814409331622 key: test_recall value: [0.75 0.66666667 0.58333333 0.75 0.81818182 0.63636364 0.63636364 0.66666667 0.58333333 0.58333333] mean value: 0.6674242424242424 key: train_recall value: [0.9047619 0.93333333 0.93333333 0.92380952 0.95283019 0.9245283 0.9245283 0.93333333 0.96190476 0.91428571] mean value: 0.9306648697214734 key: test_accuracy value: [0.625 0.75 0.5 0.75 0.69565217 0.69565217 0.65217391 0.52173913 0.69565217 0.69565217] mean value: 0.6581521739130434 key: train_accuracy value: [0.92380952 0.93333333 0.93809524 0.93809524 0.95260664 0.93838863 0.93838863 0.94312796 0.96208531 0.93364929] mean value: 0.9401579778830964 key: test_roc_auc value: [0.625 0.75 0.5 0.75 0.70075758 0.69318182 0.65151515 0.51515152 0.70075758 0.70075758] mean value: 0.6587121212121213 key: train_roc_auc value: [0.92380952 0.93333333 0.93809524 0.93809524 0.95260557 0.93845463 0.93845463 0.94308176 0.96208446 0.93355795] mean value: 0.9401572327044025 key: test_jcc value: [0.5 0.57142857 0.36842105 0.6 0.5625 0.5 0.46666667 0.42105263 0.5 0.5 ] mean value: 0.4990068922305765 key: train_jcc value: [0.85585586 0.875 0.88288288 0.88181818 0.90990991 0.88288288 0.88288288 0.89090909 0.9266055 0.87272727] mean value: 0.8861474464456116 MCC on Blind test: 0.35 MCC on Training: 0.32 Running classifier: 20 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.03198385 0.03459573 0.0139339 0.01397586 0.01387835 0.01370573 0.02309012 0.03492904 0.03508687 0.03633952] mean value: 0.025151896476745605 key: score_time value: [0.02174902 0.02291584 0.07972574 0.01194787 0.01167083 0.01164985 0.02123928 0.02139473 0.02171874 0.02166891] mean value: 0.02456808090209961 key: test_mcc value: [-0.0836242 0.41812101 0.0860663 0.0860663 0.38932432 0.13740858 0.12336594 -0.15096491 0.33371191 0.23262105] mean value: 0.15720962931207527 key: train_mcc value: [0.63821102 0.69536425 0.68574539 0.69536425 0.64929464 0.66906502 0.68751688 0.70640068 0.7076225 0.6401981 ] mean value: 0.6774782720245753 key: test_fscore value: [0.48 0.69565217 0.59259259 0.59259259 0.66666667 0.58333333 0.44444444 0.51851852 0.6 0.57142857] mean value: 0.5745228893489762 key: train_fscore value: [0.82075472 0.84615385 0.8436019 0.84615385 0.82629108 0.83091787 0.84210526 0.85024155 0.84729064 0.81553398] mean value: 0.8369044689259992 key: test_precision value: [0.46153846 0.72727273 0.53333333 0.53333333 0.7 0.53846154 0.57142857 0.46666667 0.75 0.66666667] mean value: 0.5948701298701299 key: train_precision value: [0.81308411 0.85436893 0.83962264 0.85436893 0.82242991 0.85148515 0.85436893 0.8627451 0.87755102 0.83168317] mean value: 0.846170789159659 key: test_recall value: [0.5 0.66666667 0.66666667 0.66666667 0.63636364 0.63636364 0.36363636 0.58333333 0.5 0.5 ] mean value: 0.571969696969697 key: train_recall value: [0.82857143 0.83809524 0.84761905 0.83809524 0.83018868 0.81132075 0.83018868 0.83809524 0.81904762 0.8 ] mean value: 0.8281221922731357 key: test_accuracy value: [0.45833333 0.70833333 0.54166667 0.54166667 0.69565217 0.56521739 0.56521739 0.43478261 0.65217391 0.60869565] mean value: 0.5771739130434782 key: train_accuracy value: [0.81904762 0.84761905 0.84285714 0.84761905 0.82464455 0.83412322 0.8436019 0.85308057 0.85308057 0.81990521] mean value: 0.8385578876100203 key: test_roc_auc value: [0.45833333 0.70833333 0.54166667 0.54166667 0.69318182 0.56818182 0.55681818 0.4280303 0.65909091 0.61363636] mean value: 0.5768939393939394 key: train_roc_auc value: [0.81904762 0.84761905 0.84285714 0.84761905 0.82461815 0.83423181 0.84366577 0.85300988 0.85292004 0.81981132] mean value: 0.8385399820305481 key: test_jcc value: [0.31578947 0.53333333 0.42105263 0.42105263 0.5 0.41176471 0.28571429 0.35 0.42857143 0.4 ] mean value: 0.40672784903435055 key: train_jcc value: [0.696 0.73333333 0.7295082 0.73333333 0.704 0.7107438 0.72727273 0.7394958 0.73504274 0.68852459] mean value: 0.7197254515839595 MCC on Blind test: 0.24 MCC on Training: 0.16 Running classifier: 21 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=3))]) key: fit_time value: [0.04660654 0.05163431 0.07120728 0.05229878 0.10001659 0.10776973 0.13446093 0.10261631 0.1012001 0.10342312] mean value: 0.08712337017059327 key: score_time value: [0.01258779 0.01433063 0.01413703 0.02929473 0.01848054 0.01903081 0.0242238 0.02298856 0.02449083 0.01944613] mean value: 0.019901084899902343 key: test_mcc value: [0.45834925 0.43033148 0. 0.2508726 0.31298622 0.30240737 0.12406456 0.03816905 0.25495628 0.13740858] mean value: 0.2309545399233012 key: train_mcc value: [0.56231294 0.600982 0.53335752 0.51430904 0.52605571 0.50723336 0.51662174 0.57399962 0.55453729 0.52651931] mean value: 0.5415928525458098 key: test_fscore value: [0.75862069 0.66666667 0.5 0.60869565 0.66666667 0.6 0.5 0.56 0.52631579 0.54545455] mean value: 0.5932420010090649 key: train_fscore value: [0.77669903 0.79411765 0.76555024 0.75829384 0.76415094 0.75238095 0.75829384 0.7804878 0.77725118 0.76635514] mean value: 0.7693580618820873 key: test_precision value: [0.64705882 0.77777778 0.5 0.63636364 0.61538462 0.66666667 0.55555556 0.53846154 0.71428571 0.6 ] mean value: 0.6251554328024915 key: train_precision value: [0.79207921 0.81818182 0.76923077 0.75471698 0.76415094 0.75961538 0.76190476 0.8 0.77358491 0.75229358] mean value: 0.7745758350023857 key: test_recall value: [0.91666667 0.58333333 0.5 0.58333333 0.72727273 0.54545455 0.45454545 0.58333333 0.41666667 0.5 ] mean value: 0.5810606060606062 key: train_recall value: [0.76190476 0.77142857 0.76190476 0.76190476 0.76415094 0.74528302 0.75471698 0.76190476 0.78095238 0.78095238] mean value: 0.7645103324348608 key: test_accuracy value: [0.70833333 0.70833333 0.5 0.625 0.65217391 0.65217391 0.56521739 0.52173913 0.60869565 0.56521739] mean value: 0.6106884057971015 key: train_accuracy value: [0.78095238 0.8 0.76666667 0.75714286 0.76303318 0.7535545 0.75829384 0.78672986 0.77725118 0.76303318] mean value: 0.770665763935906 key: test_roc_auc value: [0.70833333 0.70833333 0.5 0.625 0.65530303 0.64772727 0.56060606 0.51893939 0.61742424 0.56818182] mean value: 0.6109848484848486 key: train_roc_auc value: [0.78095238 0.8 0.76666667 0.75714286 0.76302785 0.75359389 0.75831087 0.78661276 0.77726864 0.7631177 ] mean value: 0.7706693620844564 key: test_jcc value: [0.61111111 0.5 0.33333333 0.4375 0.5 0.42857143 0.33333333 0.38888889 0.35714286 0.375 ] mean value: 0.4264880952380953 key: train_jcc value: [0.63492063 0.65853659 0.62015504 0.61068702 0.61832061 0.60305344 0.61068702 0.64 0.63565891 0.62121212] mean value: 0.6253231386590035 MCC on Blind test: 0.35 MCC on Training: 0.23 Running classifier: 22 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.01524639 0.01278281 0.01212788 0.01223516 0.01161861 0.01279235 0.01200771 0.01157284 0.01237106 0.0116775 ] mean value: 0.012443232536315917 key: score_time value: [0.01045036 0.01063776 0.01027274 0.00939417 0.00996089 0.00986576 0.00952387 0.01045346 0.0094738 0.00995851] mean value: 0.00999913215637207 key: test_mcc value: [ 0.60246408 0.09166985 0. 0.41812101 0.48856385 0.13740858 0.3030303 -0.04545455 0.47727273 0.31298622] mean value: 0.27860620710809664 key: train_mcc value: [0.52390457 0.60540551 0.63913958 0.60273974 0.56411178 0.52703602 0.61375841 0.58537968 0.57399962 0.54507057] mean value: 0.5780545496381201 key: test_fscore value: [0.81481481 0.42105263 0.53846154 0.69565217 0.75 0.58333333 0.63636364 0.5 0.75 0.63636364] mean value: 0.632604176482895 key: train_fscore value: [0.75961538 0.78571429 0.81372549 0.79 0.78095238 0.75728155 0.79802956 0.78 0.7804878 0.76923077] mean value: 0.7815037225635252 key: test_precision value: [0.73333333 0.57142857 0.5 0.72727273 0.69230769 0.53846154 0.63636364 0.5 0.75 0.7 ] mean value: 0.6349167499167498 key: train_precision value: [0.76699029 0.84615385 0.83838384 0.83157895 0.78846154 0.78 0.83505155 0.82105263 0.8 0.77669903] mean value: 0.8084371668726694 key: test_recall value: [0.91666667 0.33333333 0.58333333 0.66666667 0.81818182 0.63636364 0.63636364 0.5 0.75 0.58333333] mean value: 0.6424242424242423 key: train_recall value: [0.75238095 0.73333333 0.79047619 0.75238095 0.77358491 0.73584906 0.76415094 0.74285714 0.76190476 0.76190476] mean value: 0.7568823000898472 key: test_accuracy value: [0.79166667 0.54166667 0.5 0.70833333 0.73913043 0.56521739 0.65217391 0.47826087 0.73913043 0.65217391] mean value: 0.6367753623188406 key: train_accuracy value: [0.76190476 0.8 0.81904762 0.8 0.78199052 0.76303318 0.8056872 0.79146919 0.78672986 0.77251185] mean value: 0.7882374181900249 key: test_roc_auc value: [0.79166667 0.54166667 0.5 0.70833333 0.74242424 0.56818182 0.65151515 0.47727273 0.73863636 0.65530303] mean value: 0.6375 key: train_roc_auc value: [0.76190476 0.8 0.81904762 0.8 0.78203055 0.76316262 0.805885 0.79123989 0.78661276 0.77246181] mean value: 0.788234501347709 key: test_jcc value: [0.6875 0.26666667 0.36842105 0.53333333 0.6 0.41176471 0.46666667 0.33333333 0.6 0.46666667] mean value: 0.47343524251805985 key: train_jcc value: [0.6124031 0.64705882 0.68595041 0.65289256 0.640625 0.609375 0.66393443 0.63934426 0.64 0.625 ] mean value: 0.6416583588035807 MCC on Blind test: 0.33 MCC on Training: 0.28 Running classifier: 23 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.01220822 0.0148859 0.01712203 0.01577616 0.01607013 0.01419306 0.01648164 0.01503444 0.01477265 0.01501298] mean value: 0.015155720710754394 key: score_time value: [0.00858951 0.011096 0.01108456 0.01148939 0.01146889 0.01163244 0.0116396 0.01156497 0.01167417 0.0115521 ] mean value: 0.011179161071777344 key: test_mcc value: [0.2508726 0.27500955 0. 0.38490018 0.38932432 0.20412415 0.14607664 0.14607664 0.17236256 0.2096648 ] mean value: 0.2178411438675814 key: train_mcc value: [0.60540551 0.47675805 0.70278193 0.54374562 0.59259268 0.32512967 0.47392519 0.55671678 0.63495227 0.38191147] mean value: 0.5293919169048058 key: test_fscore value: [0.60869565 0.68965517 0.45454545 0.73333333 0.66666667 0.66666667 0.28571429 0.6875 0.44444444 0.375 ] mean value: 0.5612221675958557 key: train_fscore value: [0.78571429 0.765625 0.82352941 0.77777778 0.8 0.71380471 0.53793103 0.79518072 0.80203046 0.42962963] mean value: 0.7231223032918229 key: test_precision value: [0.63636364 0.58823529 0.5 0.61111111 0.7 0.5 0.66666667 0.55 0.66666667 0.75 ] mean value: 0.6169043374925729 key: train_precision value: [0.84615385 0.64900662 0.93902439 0.75675676 0.78899083 0.55497382 1. 0.6875 0.85869565 0.96666667] mean value: 0.8047768582189242 key: test_recall value: [0.58333333 0.83333333 0.41666667 0.91666667 0.63636364 1. 0.18181818 0.91666667 0.33333333 0.25 ] mean value: 0.6068181818181817 key: train_recall value: [0.73333333 0.93333333 0.73333333 0.8 0.81132075 1. 0.36792453 0.94285714 0.75238095 0.27619048] mean value: 0.7350673854447439 key: test_accuracy value: [0.625 0.625 0.5 0.66666667 0.69565217 0.52173913 0.56521739 0.56521739 0.56521739 0.56521739] mean value: 0.5894927536231883 key: train_accuracy value: [0.8 0.71428571 0.84285714 0.77142857 0.79620853 0.5971564 0.68246445 0.75829384 0.81516588 0.63507109] mean value: 0.7412931618144888 key: test_roc_auc value: [0.625 0.625 0.5 0.66666667 0.69318182 0.54166667 0.54924242 0.54924242 0.57575758 0.57954545] mean value: 0.5905303030303031 key: train_roc_auc value: [0.8 0.71428571 0.84285714 0.77142857 0.79613657 0.5952381 0.68396226 0.75916442 0.81486972 0.63337826] mean value: 0.7411320754716981 key: test_jcc value: [0.4375 0.52631579 0.29411765 0.57894737 0.5 0.5 0.16666667 0.52380952 0.28571429 0.23076923] mean value: 0.40438405119132675 key: train_jcc value: [0.64705882 0.62025316 0.7 0.63636364 0.66666667 0.55497382 0.36792453 0.66 0.66949153 0.27358491] mean value: 0.5796317072492199 MCC on Blind test: 0.57 MCC on Training: 0.22 Running classifier: 24 Model_name: XGBoost Model func: /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:463: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_CV['source_data'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:490: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_BT['source_data'] = 'BT' XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.1202786 0.20461535 0.06621122 0.09809661 0.14126515 0.07549524 0.10464358 0.07893324 0.06769824 0.07654476] mean value: 0.10337820053100585 key: score_time value: [0.01134729 0.01078391 0.01068568 0.01145148 0.01229453 0.01239371 0.01232409 0.01100636 0.01103783 0.01252556] mean value: 0.011585044860839843 key: test_mcc value: [0.3380617 0.3380617 0. 0.35355339 0.3030303 0.21452908 0.48075018 0.12878788 0.31298622 0.33371191] mean value: 0.28034723588661625 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.63636364 0.63636364 0.53846154 0.71428571 0.63636364 0.52631579 0.7 0.58333333 0.63636364 0.6 ] mean value: 0.6207850921008816 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.7 0.7 0.5 0.625 0.63636364 0.625 0.77777778 0.58333333 0.7 0.75 ] mean value: 0.6597474747474747 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [0.58333333 0.58333333 0.58333333 0.83333333 0.63636364 0.45454545 0.63636364 0.58333333 0.58333333 0.5 ] mean value: 0.5977272727272728 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.66666667 0.66666667 0.5 0.66666667 0.65217391 0.60869565 0.73913043 0.56521739 0.65217391 0.65217391] mean value: 0.6369565217391304 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.66666667 0.66666667 0.5 0.66666667 0.65151515 0.60227273 0.73484848 0.56439394 0.65530303 0.65909091] mean value: 0.6367424242424242 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.46666667 0.46666667 0.36842105 0.55555556 0.46666667 0.35714286 0.53846154 0.41176471 0.46666667 0.42857143] mean value: 0.45265838049119783 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.37 MCC on Training: 0.28 Extracting tts_split_name: sl Total cols in each df: CV df: 8 metaDF: 17 Adding column: Model_name Total cols in bts df: BT_df: 8 First proceeding to rowbind CV and BT dfs: Final output should have: 25 columns Combinig 2 using pd.concat by row ~ rowbind Checking Dims of df to combine: Dim of CV: (24, 8) Dim of BT: (24, 8) 8 Number of Common columns: 8 These are: ['source_data', 'Precision', 'JCC', 'MCC', 'Recall', 'Accuracy', 'F1', 'ROC_AUC'] Concatenating dfs with different resampling methods [WF]: Split type: sl No. of dfs combining: 2 PASS: 2 dfs successfully combined nrows in combined_df_wf: 48 ncols in combined_df_wf: 8 PASS: proceeding to merge metadata with CV and BT dfs Adding column: Model_name ========================================================= SUCCESS: Ran multiple classifiers ======================================================= ============================================================== Running several classification models (n): 24 List of models: ('AdaBoost Classifier', AdaBoostClassifier(random_state=42)) ('Bagging Classifier', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3)) ('Decision Tree', DecisionTreeClassifier(random_state=42)) ('Extra Tree', ExtraTreeClassifier(random_state=42)) ('Extra Trees', ExtraTreesClassifier(random_state=42)) ('Gradient Boosting', GradientBoostingClassifier(random_state=42)) ('Gaussian NB', GaussianNB()) ('Gaussian Process', GaussianProcessClassifier(random_state=42)) ('K-Nearest Neighbors', KNeighborsClassifier()) ('LDA', LinearDiscriminantAnalysis()) ('Logistic Regression', LogisticRegression(random_state=42)) ('Logistic RegressionCV', LogisticRegressionCV(cv=3, random_state=42)) ('MLP', MLPClassifier(max_iter=500, random_state=42)) ('Multinomial', MultinomialNB()) ('Naive Bayes', BernoulliNB()) ('Passive Aggresive', PassiveAggressiveClassifier(n_jobs=12, random_state=42)) ('QDA', QuadraticDiscriminantAnalysis()) ('Random Forest', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42)) ('Random Forest2', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42)) ('Ridge Classifier', RidgeClassifier(random_state=42)) ('Ridge ClassifierCV', RidgeClassifierCV(cv=3)) ('SVC', SVC(random_state=42)) ('Stochastic GDescent', SGDClassifier(n_jobs=12, random_state=42)) ('XGBoost', XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0)) ================================================================ Running classifier: 1 Model_name: AdaBoost Classifier Model func: AdaBoostClassifier(random_state=42) Running model pipeline: [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. 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Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.4s remaining: 0.8s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.4s remaining: 0.8s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.5s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.4s remaining: 0.8s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.5s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... @ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþÿÿÿÿÿÿÿÀ9@ƒ† °¬)Û?8ãZ$¨©Ð?€C@„…£ÉÍÀ? >ŸÖ•»?€A@ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþÿÿÿÿÿÿÿÀ@ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþÿÿÿÿÿÿÿÀ€@@ÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿÿþÿÿÿÿÿÿÿÀ@ÿ °  !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOBuilding estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.4s remaining: 0.8s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.5s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.4s remaining: 0.7s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.4s remaining: 0.8s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.5s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.4s remaining: 0.8s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.5s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.4s remaining: 0.8s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.5s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.4s remaining: 0.8s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.5s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.4s remaining: 0.8s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.4s remaining: 0.1s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.4s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... ð?ð?ð?ð?ð?ð?ð?@ð?ð?@ð?@@ð?@ð?@@@ð?ð?ð?ð?@ð?[Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 3.4s remaining: 6.9s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 3.6s remaining: 7.3s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 3.7s remaining: 7.3s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 3.7s remaining: 7.5s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 3.8s remaining: 7.5s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 3.8s remaining: 7.6s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 3.8s remaining: 1.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 3.8s remaining: 1.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 3.8s remaining: 1.3s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 3.8s remaining: 7.7s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 3.8s remaining: 7.7s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 3.8s remaining: 7.7s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 3.9s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 3.9s remaining: 1.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 3.9s remaining: 1.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 3.9s remaining: 1.3s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 3.9s remaining: 1.3s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 3.9s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 3.9s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 3.9s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 3.9s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 4.0s remaining: 1.3s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 4.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 4.0s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 4.0s remaining: 1.3s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 4.0s remaining: 7.9s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 4.0s finished [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 4.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 4.0s remaining: 1.3s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 4.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.1s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.1s remaining: 0.0s [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.1s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Using backend ThreadingBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.1s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.0s finished Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', AdaBoostClassifier(random_state=42))]) key: fit_time value: [0.33940506 0.3562305 0.33009768 0.30898237 0.30743265 0.30723166 0.30597997 0.30811524 0.30880475 0.30795836] mean value: 0.3180238246917725 key: score_time value: [0.01840115 0.01847768 0.01626134 0.01606226 0.01600337 0.01618099 0.01604986 0.01613355 0.01608038 0.0160737 ] mean value: 0.01657242774963379 key: test_mcc value: [0.71930611 0.63330337 0.70406149 0.60184923 0.65926347 0.70393163 0.63011044 0.66485241 0.63274178 0.77846222] mean value: 0.6727882148836837 key: train_mcc value: [0.82651568 0.77968942 0.83152886 0.78298281 0.82126569 0.78957872 0.75967062 0.80969164 0.74979407 0.80980204] mean value: 0.796051955437029 key: test_fscore value: [0.86131387 0.82269504 0.85294118 0.8057554 0.83783784 0.85507246 0.82014388 0.84137931 0.82517483 0.89208633] mean value: 0.8414400129181118 key: train_fscore value: [0.91492777 0.89123377 0.91740176 0.89285714 0.91216761 0.89593496 0.87973641 0.90630048 0.87479407 0.90645161] mean value: 0.8991805583619117 key: test_precision value: [0.84285714 0.78378378 0.84057971 0.77777778 0.7654321 0.84285714 0.8028169 0.79220779 0.78666667 0.87323944] mean value: 0.8108218453088835 key: train_precision value: [0.89341693 0.87980769 0.89514867 0.88141026 0.89415482 0.88443018 0.87973641 0.88906498 0.87479407 0.8878357 ] mean value: 0.8859799698293898 key: test_recall value: [0.88059701 0.86567164 0.86567164 0.8358209 0.92537313 0.86764706 0.83823529 0.89705882 0.86764706 0.91176471] mean value: 0.875548726953468 key: train_recall value: [0.9375 0.90296053 0.94078947 0.90460526 0.93092105 0.907743 0.87973641 0.92421746 0.87479407 0.92586491] mean value: 0.9129132164224399 key: test_accuracy value: [0.85925926 0.81481481 0.85185185 0.8 0.82222222 0.85185185 0.81481481 0.82962963 0.81481481 0.88888889] mean value: 0.8348148148148148 key: train_accuracy value: [0.9127572 0.88971193 0.91522634 0.89135802 0.91028807 0.89465021 0.87983539 0.90452675 0.87489712 0.90452675] mean value: 0.8977777777777776 key: test_roc_auc value: [0.85941615 0.81518876 0.85195347 0.80026339 0.82298068 0.85173398 0.81464004 0.82912643 0.81442054 0.88871817] mean value: 0.8348441615452151 key: train_roc_auc value: [0.91273682 0.88970102 0.91520528 0.89134711 0.91027107 0.89466097 0.87983531 0.90454294 0.87489703 0.9045443 ] mean value: 0.8977741860313883 key: test_jcc value: [0.75641026 0.69879518 0.74358974 0.6746988 0.72093023 0.74683544 0.69512195 0.72619048 0.70238095 0.80519481] mean value: 0.7270147836485474 key: train_jcc value: [0.84319527 0.80380673 0.84740741 0.80645161 0.83851852 0.81148748 0.78529412 0.82865583 0.77745242 0.82890855] mean value: 0.8171177944280771 MCC on Blind test: 0.33 MCC on Training: 0.67 Running classifier: 2 Model_name: Bagging Classifier Model func: BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BaggingClassifier(n_estimators=100, n_jobs=12, oob_score=True, random_state=42, verbose=3))]) key: fit_time value: [0.53435469 0.69483972 0.68969631 0.68084526 0.61432719 0.66407633 0.54805756 0.65011883 0.6844933 0.62394142] mean value: 0.6384750604629517 key: score_time value: [0.04186082 0.06779528 0.07151771 0.06861544 0.07245255 0.06907344 0.05558801 0.05108261 0.05590153 0.08718228] mean value: 0.06410696506500244 key: test_mcc value: [0.97080134 0.9565124 0.92851083 0.91478147 0.91478147 0.92843493 0.95565315 0.9707887 0.9707887 0.95648435] mean value: 0.9467537336699872 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.98529412 0.97810219 0.96402878 0.95714286 0.95714286 0.96453901 0.97810219 0.98550725 0.98550725 0.97841727] mean value: 0.9733783754506105 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.97101449 0.95714286 0.93055556 0.91780822 0.91780822 0.93150685 0.97101449 0.97142857 0.97142857 0.95774648] mean value: 0.9497454307607274 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 0.98529412 1. 1. 1. ] mean value: 0.9985294117647058 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.98518519 0.97777778 0.96296296 0.95555556 0.95555556 0.96296296 0.97777778 0.98518519 0.98518519 0.97777778] mean value: 0.9725925925925927 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.98529412 0.97794118 0.96323529 0.95588235 0.95588235 0.96268657 0.97772169 0.98507463 0.98507463 0.97761194] mean value: 0.9726404741000877 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.97101449 0.95714286 0.93055556 0.91780822 0.91780822 0.93150685 0.95714286 0.97142857 0.97142857 0.95774648] mean value: 0.9483582671996509 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 Building estimator 1 of 9 for this parallel run (total 100)... 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Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/joblib/externals/loky/process_executor.py:702: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak. warnings.warn( [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.6s remaining: 1.2s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.7s remaining: 0.2s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 1.0s finished [Parallel(n_jobs=12)]: Using backend LokyBackend with 12 concurrent workers. [Parallel(n_jobs=12)]: Done 4 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 9 out of 12 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=12)]: Done 12 out of 12 | elapsed: 0.3s finished MCC on Blind test: 0.49 MCC on Training: 0.95 Running classifier: 3 Model_name: Decision Tree Model func: DecisionTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', DecisionTreeClassifier(random_state=42))]) key: fit_time value: [0.08529019 0.05498195 0.05503845 0.05053735 0.06055617 0.05929065 0.05295444 0.06105685 0.05813432 0.05814338] mean value: 0.05959837436676026 key: score_time value: [0.00956917 0.00949836 0.01014042 0.00961757 0.00957727 0.00955176 0.01013541 0.00956535 0.00957298 0.01017189] mean value: 0.009740018844604492 key: test_mcc value: [0.92851083 0.91478147 0.87458526 0.84854147 0.83572504 0.87435075 0.87126224 0.92843493 0.9010773 0.91467353] mean value: 0.8891942819109573 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.96402878 0.95714286 0.93706294 0.92413793 0.91780822 0.93793103 0.93706294 0.96453901 0.95104895 0.95774648] mean value: 0.9448509129956862 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.93055556 0.91780822 0.88157895 0.85897436 0.84810127 0.88311688 0.89333333 0.93150685 0.90666667 0.91891892] mean value: 0.8970560998250073 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 0.98529412 1. 1. 1. ] mean value: 0.9985294117647058 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.96296296 0.95555556 0.93333333 0.91851852 0.91111111 0.93333333 0.93333333 0.96296296 0.94814815 0.95555556] mean value: 0.9414814814814815 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.96323529 0.95588235 0.93382353 0.91911765 0.91176471 0.93283582 0.93294557 0.96268657 0.94776119 0.95522388] mean value: 0.9415276558384548 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.93055556 0.91780822 0.88157895 0.85897436 0.84810127 0.88311688 0.88157895 0.93150685 0.90666667 0.91891892] mean value: 0.8958806612285161 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.23 MCC on Training: 0.89 Running classifier: 4 Model_name: Extra Tree Model func: ExtraTreeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreeClassifier(random_state=42))]) key: fit_time value: [0.01320362 0.01322269 0.01327348 0.01340413 0.01322913 0.01340032 0.0132544 0.0132401 0.013345 0.01318836] mean value: 0.013276124000549316 key: score_time value: [0.00925136 0.00917482 0.00924778 0.00919342 0.00926137 0.00922918 0.00905776 0.00903916 0.00906181 0.00907159] mean value: 0.009158825874328614 key: test_mcc value: [0.90122245 0.90122245 0.9424184 0.84854147 0.87458526 0.82256727 0.89870563 0.88763877 0.9010773 0.9010773 ] mean value: 0.8879056308753762 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.95035461 0.95035461 0.97101449 0.92413793 0.93706294 0.91275168 0.95035461 0.94444444 0.95104895 0.95104895] mean value: 0.9442573215032974 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.90540541 0.90540541 0.94366197 0.85897436 0.88157895 0.83950617 0.91780822 0.89473684 0.90666667 0.90666667] mean value: 0.896041065644076 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 0.98529412 1. 1. 1. ] mean value: 0.9985294117647058 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.94814815 0.94814815 0.97037037 0.91851852 0.93333333 0.9037037 0.94814815 0.94074074 0.94814815 0.94814815] mean value: 0.9407407407407407 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.94852941 0.94852941 0.97058824 0.91911765 0.93382353 0.90298507 0.94787094 0.94029851 0.94776119 0.94776119] mean value: 0.9407265144863917 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.90540541 0.90540541 0.94366197 0.85897436 0.88157895 0.83950617 0.90540541 0.89473684 0.90666667 0.90666667] mean value: 0.8948007842668083 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.16 MCC on Training: 0.89 Running classifier: 5 Model_name: Extra Trees Model func: ExtraTreesClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', ExtraTreesClassifier(random_state=42))]) key: fit_time value: [0.19760418 0.19664359 0.20481038 0.21389723 0.2135067 0.20801425 0.21132207 0.22400808 0.22487903 0.22436929] mean value: 0.21190547943115234 key: score_time value: [0.01967978 0.01962447 0.02019 0.02151108 0.02098608 0.02172661 0.02065706 0.02157354 0.02249169 0.02181387] mean value: 0.021025419235229492 key: test_mcc value: [1. 1. 0.98529412 1. 0.92851083 0.95648435 0.95565315 0.98529091 0.98529091 0.98529091] mean value: 0.9781815181277645 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [1. 1. 0.99259259 1. 0.96402878 0.97841727 0.97810219 0.99270073 0.99270073 0.99270073] mean value: 0.9891243015320104 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [1. 1. 0.98529412 1. 0.93055556 0.95774648 0.97101449 0.98550725 0.98550725 0.98550725] mean value: 0.9801132383959914 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 0.98529412 1. 1. 1. ] mean value: 0.9985294117647058 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [1. 1. 0.99259259 1. 0.96296296 0.97777778 0.97777778 0.99259259 0.99259259 0.99259259] mean value: 0.9888888888888887 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [1. 1. 0.99264706 1. 0.96323529 0.97761194 0.97772169 0.99253731 0.99253731 0.99253731] mean value: 0.9888827919227392 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [1. 1. 0.98529412 1. 0.93055556 0.95774648 0.95714286 0.98550725 0.98550725 0.98550725] mean value: 0.9787260748349148 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: -0.07 MCC on Training: 0.98 Running classifier: 6 Model_name: Gradient Boosting Model func: GradientBoostingClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GradientBoostingClassifier(random_state=42))]) key: fit_time value: [1.4302702 1.38267612 1.38187504 1.3776238 1.37760615 1.38221312 1.37247682 1.38436604 1.39916873 1.39634871] mean value: 1.3884624719619751 key: score_time value: [0.00991273 0.00947547 0.0098784 0.00955606 0.00963259 0.00977993 0.00961399 0.01042604 0.00967741 0.00960755] mean value: 0.009756016731262206 key: test_mcc value: [0.88147498 0.90122245 0.89721083 0.84168488 0.86149266 0.84144718 0.88297618 0.9010773 0.89870563 0.9010773 ] mean value: 0.8808369379333705 key: train_mcc value: [0.96572581 0.96411966 0.96888264 0.9623596 0.97399985 0.97212347 0.96243407 0.95906804 0.97056019 0.97079728] mean value: 0.9670070626380317 key: test_fscore value: [0.94029851 0.95035461 0.94890511 0.92198582 0.93055556 0.92307692 0.94285714 0.95104895 0.95035461 0.95104895] mean value: 0.9410486176000254 key: train_fscore value: [0.9829407 0.98214286 0.98451508 0.9812856 0.98701299 0.98609975 0.9812856 0.9796251 0.98531811 0.98538961] mean value: 0.9835615390926715 key: test_precision value: [0.94029851 0.90540541 0.92857143 0.87837838 0.87012987 0.88 0.91666667 0.90666667 0.91780822 0.90666667] mean value: 0.9050591809125852 key: train_precision value: [0.97110754 0.96955128 0.97576737 0.97101449 0.97435897 0.9788961 0.96945338 0.96935484 0.97576737 0.9712 ] mean value: 0.9726471345557733 key: test_recall value: [0.94029851 1. 0.97014925 0.97014925 1. 0.97058824 0.97058824 1. 0.98529412 1. ] mean value: 0.9807067603160669 key: train_recall value: [0.99506579 0.99506579 0.99342105 0.99177632 1. 0.99341021 0.99341021 0.99011532 0.99505766 1. ] mean value: 0.9947322357582589 key: test_accuracy value: [0.94074074 0.94814815 0.94814815 0.91851852 0.92592593 0.91851852 0.94074074 0.94814815 0.94814815 0.94814815] mean value: 0.9385185185185184 key: train_accuracy value: [0.98271605 0.981893 0.98436214 0.98106996 0.98683128 0.98600823 0.98106996 0.97942387 0.98518519 0.98518519] mean value: 0.9833744855967079 key: test_roc_auc value: [0.94073749 0.94852941 0.94830992 0.91889816 0.92647059 0.91812994 0.940518 0.94776119 0.94787094 0.94776119] mean value: 0.9384986830553117 key: train_roc_auc value: [0.98270588 0.98188215 0.98435468 0.98106114 0.98682043 0.98601432 0.98108011 0.97943266 0.9851933 0.98519737] mean value: 0.9833742033729298 key: test_jcc value: [0.88732394 0.90540541 0.90277778 0.85526316 0.87012987 0.85714286 0.89189189 0.90666667 0.90540541 0.90666667] mean value: 0.888867364264325 key: train_jcc value: [0.96645367 0.96491228 0.96950241 0.96325879 0.97435897 0.97258065 0.96325879 0.9600639 0.97106109 0.9712 ] mean value: 0.9676650544944231 MCC on Blind test: 0.43 MCC on Training: 0.88 Running classifier: 7 Model_name: Gaussian NB Model func: GaussianNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianNB())]) key: fit_time value: [0.01370859 0.01407266 0.01573968 0.01570201 0.01600361 0.01574564 0.01485085 0.01477647 0.01512265 0.01541734] mean value: 0.0151139497756958 key: score_time value: [0.01051283 0.01063251 0.01117373 0.01135468 0.01121783 0.01137662 0.01058626 0.01136017 0.01135921 0.01054931] mean value: 0.01101231575012207 key: test_mcc value: [0.39422132 0.27398464 0.39397368 0.27518764 0.408352 0.37119084 0.42230026 0.38097018 0.52703343 0.39397368] mean value: 0.38411876709912524 key: train_mcc value: [0.43396438 0.42059154 0.41069982 0.43239071 0.42111304 0.40749614 0.41070301 0.42302831 0.40576227 0.39949464] mean value: 0.4165243851973116 key: test_fscore value: [0.67716535 0.63157895 0.70503597 0.64748201 0.71014493 0.71523179 0.71111111 0.671875 0.75757576 0.6870229 ] mean value: 0.6914223772376571 key: train_fscore value: [0.72168285 0.71194763 0.70607553 0.72154964 0.70320405 0.70636215 0.70559211 0.69337979 0.70288066 0.69301934] mean value: 0.7065693744391728 key: test_precision value: [0.71666667 0.63636364 0.68055556 0.625 0.69014085 0.65060241 0.71641791 0.71666667 0.78125 0.71428571] mean value: 0.6927949404694977 key: train_precision value: [0.71019108 0.70846906 0.70491803 0.70839937 0.72145329 0.69951535 0.7044335 0.73567468 0.70230263 0.70790378] mean value: 0.7103260757290821 key: test_recall value: [0.64179104 0.62686567 0.73134328 0.67164179 0.73134328 0.79411765 0.70588235 0.63235294 0.73529412 0.66176471] mean value: 0.6932396839332748 key: train_recall value: [0.73355263 0.71546053 0.70723684 0.73519737 0.68585526 0.71334432 0.70675453 0.65568369 0.70345964 0.67874794] mean value: 0.7035292746900199 key: test_accuracy value: [0.6962963 0.63703704 0.6962963 0.63703704 0.7037037 0.68148148 0.71111111 0.68888889 0.76296296 0.6962963 ] mean value: 0.6911111111111111 key: train_accuracy value: [0.71687243 0.71028807 0.70534979 0.71604938 0.71028807 0.7037037 0.70534979 0.71028807 0.70288066 0.69958848] mean value: 0.7080658436213992 key: test_roc_auc value: [0.69589552 0.63696225 0.69655399 0.63729148 0.70390694 0.68064091 0.71115013 0.6893108 0.76316945 0.69655399] mean value: 0.6911435469710272 key: train_roc_auc value: [0.71685869 0.71028381 0.70534824 0.71603361 0.71030819 0.70371163 0.70535095 0.71024316 0.70288113 0.69957134] mean value: 0.7080590750455216 key: test_jcc value: [0.51190476 0.46153846 0.54444444 0.4787234 0.5505618 0.55670103 0.55172414 0.50588235 0.6097561 0.52325581] mean value: 0.5294492303210305 key: train_jcc value: [0.56455696 0.55273189 0.54568528 0.56439394 0.54226268 0.54602774 0.54510801 0.53066667 0.54187817 0.53024453] mean value: 0.5463555870007957 MCC on Blind test: 0.4 MCC on Training: 0.38 Running classifier: 8 Model_name: Gaussian Process Model func: GaussianProcessClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', GaussianProcessClassifier(random_state=42))]) key: fit_time value: [0.83273697 0.81287408 0.6806953 0.8211422 0.84218359 0.85273123 0.99310231 0.77349591 0.91463351 0.86188793] mean value: 0.8385483026504517 key: score_time value: [0.05354571 0.02976418 0.0287354 0.03745675 0.05634642 0.05931616 0.05770683 0.03095627 0.05653811 0.04381108] mean value: 0.04541769027709961 key: test_mcc value: [0.76613499 0.75237852 0.81484885 0.72855917 0.83572504 0.6933147 0.79559839 0.80951774 0.85218556 0.83532101] mean value: 0.7883583964395515 key: train_mcc value: [0.93042737 0.91682319 0.94173935 0.91682319 0.94239411 0.9412525 0.92842515 0.93000823 0.93028132 0.93200398] mean value: 0.931017838222157 key: test_fscore value: [0.88571429 0.87943262 0.90909091 0.86896552 0.91780822 0.85526316 0.90140845 0.90780142 0.92753623 0.91891892] mean value: 0.8971939733179788 key: train_fscore value: [0.96551724 0.95890411 0.97106109 0.95890411 0.97124601 0.97082658 0.96451613 0.96529459 0.96540628 0.96623794] mean value: 0.965791407913013 key: test_precision value: [0.84931507 0.83783784 0.85526316 0.80769231 0.84810127 0.77380952 0.86486486 0.87671233 0.91428571 0.85 ] mean value: 0.8477882069468045 key: train_precision value: [0.94209703 0.9399684 0.94968553 0.9399684 0.94409938 0.9553429 0.94470774 0.94620253 0.94339623 0.94348509] mean value: 0.9448953236954502 key: test_recall value: [0.92537313 0.92537313 0.97014925 0.94029851 1. 0.95588235 0.94117647 0.94117647 0.94117647 1. ] mean value: 0.9540605794556629 key: train_recall value: [0.99013158 0.97861842 0.99342105 0.97861842 1. 0.98682043 0.98517298 0.98517298 0.98846787 0.99011532] mean value: 0.9876539061822596 key: test_accuracy value: [0.88148148 0.87407407 0.9037037 0.85925926 0.91111111 0.83703704 0.8962963 0.9037037 0.92592593 0.91111111] mean value: 0.8903703703703704 key: train_accuracy value: [0.96460905 0.95802469 0.97037037 0.95802469 0.97037037 0.97037037 0.96378601 0.96460905 0.96460905 0.9654321 ] mean value: 0.9650205761316872 key: test_roc_auc value: [0.88180421 0.87445127 0.90419227 0.85985514 0.91176471 0.83615013 0.89596137 0.90342406 0.92581212 0.91044776] mean value: 0.8903863037752414 key: train_roc_auc value: [0.96458803 0.95800773 0.97035138 0.95800773 0.97034596 0.9703839 0.9638036 0.96462596 0.96462867 0.9654524 ] mean value: 0.9650195363305298 key: test_jcc value: [0.79487179 0.78481013 0.83333333 0.76829268 0.84810127 0.74712644 0.82051282 0.83116883 0.86486486 0.85 ] mean value: 0.8143082156865147 key: train_jcc value: [0.93333333 0.92105263 0.94375 0.92105263 0.94409938 0.94330709 0.93146417 0.93291732 0.93312597 0.93468118] mean value: 0.9338783707100671 MCC on Blind test: 0.45 MCC on Training: 0.79 Running classifier: 9 Model_name: K-Nearest Neighbors Model func: KNeighborsClassifier() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', KNeighborsClassifier())]) key: fit_time value: [0.01622725 0.01166821 0.01131105 0.0131886 0.01234698 0.014184 0.01331258 0.01203656 0.01296473 0.01180768] mean value: 0.012904763221740723 key: score_time value: [0.02558541 0.01812077 0.01963234 0.01955414 0.01832438 0.02400231 0.02284098 0.01816106 0.01745605 0.02266669] mean value: 0.02063441276550293 key: test_mcc value: [0.6463268 0.67861469 0.64212511 0.56439739 0.68236125 0.55637466 0.71471317 0.60639056 0.71471317 0.66777816] mean value: 0.6473794949742714 key: train_mcc value: [0.77404896 0.78439035 0.77988713 0.77345869 0.78185002 0.78363522 0.77322835 0.77019953 0.77378543 0.77604361] mean value: 0.7770527286569586 key: test_fscore value: [0.83221477 0.8427673 0.82993197 0.79738562 0.84615385 0.79754601 0.86486486 0.81818182 0.86486486 0.84353741] mean value: 0.8337448475703623 key: train_fscore value: [0.88971132 0.89450223 0.89269747 0.88954781 0.89352197 0.89438202 0.88955224 0.88838951 0.88971684 0.89088191] mean value: 0.8912903327408325 key: test_precision value: [0.75609756 0.72826087 0.7625 0.70930233 0.74157303 0.68421053 0.8 0.73255814 0.8 0.78481013] mean value: 0.7499312582263039 key: train_precision value: [0.80888291 0.81571816 0.81607629 0.8097166 0.81632653 0.82005495 0.81309686 0.81456044 0.8122449 0.81532148] mean value: 0.8141999110608118 key: test_recall value: [0.92537313 1. 0.91044776 0.91044776 0.98507463 0.95588235 0.94117647 0.92647059 0.94117647 0.91176471] mean value: 0.9407813871817383 key: train_recall value: [0.98848684 0.99013158 0.98519737 0.98684211 0.98684211 0.98352554 0.98187809 0.97693575 0.98352554 0.98187809] mean value: 0.9845242998352555 key: test_accuracy value: [0.81481481 0.81481481 0.81481481 0.77037037 0.82222222 0.75555556 0.85185185 0.79259259 0.85185185 0.82962963] mean value: 0.8118518518518518 key: train_accuracy value: [0.87736626 0.88312757 0.88148148 0.87736626 0.88230453 0.88395062 0.8781893 0.87736626 0.8781893 0.87983539] mean value: 0.8799176954732509 key: test_roc_auc value: [0.81562774 0.81617647 0.815518 0.77140035 0.82341967 0.75406058 0.85118525 0.7915935 0.85118525 0.82901668] mean value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( 0.8119183494293241 key: train_roc_auc value: [0.87727472 0.88303943 0.88139605 0.87727608 0.88221842 0.8840325 0.87827457 0.87744814 0.87827593 0.87991931] mean value: 0.8799155141767103 key: test_jcc value: [0.71264368 0.72826087 0.70930233 0.66304348 0.73333333 0.66326531 0.76190476 0.69230769 0.76190476 0.72941176] mean value: 0.7155377971847282 key: train_jcc value: [0.80133333 0.80913978 0.80619112 0.80106809 0.80753701 0.80894309 0.80107527 0.79919137 0.80134228 0.8032345 ] mean value: 0.8039055854411284 MCC on Blind test: 0.26 MCC on Training: 0.65 Running classifier: 10 Model_name: LDA Model func: LinearDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LinearDiscriminantAnalysis())]) key: fit_time value: [0.06205177 0.07900739 0.08814812 0.06977868 0.08799314 0.06811118 0.08556771 0.06808519 0.08944488 0.06853008] mean value: 0.07667181491851807 key: score_time value: [0.01937819 0.01261067 0.01250458 0.01249814 0.01257348 0.01964736 0.01257539 0.0124588 0.01272082 0.01966667] mean value: 0.014663410186767579 key: test_mcc value: [0.64442493 0.53004547 0.60184923 0.46769271 0.54399701 0.43207877 0.45385143 0.54072449 0.57756797 0.52922033] mean value: 0.5321452355842036 key: train_mcc value: [0.64671644 0.69305694 0.62525082 0.65387126 0.66351588 0.66513518 0.66037197 0.64694794 0.65748487 0.6543359 ] mean value: 0.6566687211831278 key: test_fscore value: [0.82089552 0.77464789 0.8057554 0.73913043 0.78014184 0.74172185 0.74125874 0.77372263 0.80272109 0.77777778] mean value: 0.7757773173663451 key: train_fscore value: [0.82703138 0.85173502 0.81642512 0.83160415 0.83586869 0.83627608 0.83426741 0.82730924 0.83346614 0.83214002] mean value: 0.8326123242960725 key: test_precision value: [0.82089552 0.73333333 0.77777778 0.71830986 0.74324324 0.6746988 0.70666667 0.76811594 0.74683544 0.73684211] mean value: 0.7426718688074851 key: train_precision value: [0.80944882 0.81818182 0.79968454 0.80775194 0.81435257 0.81533646 0.81152648 0.80721003 0.80709877 0.80461538] mean value: 0.8095206816123678 key: test_recall value: [0.82089552 0.82089552 0.8358209 0.76119403 0.82089552 0.82352941 0.77941176 0.77941176 0.86764706 0.82352941] mean value: 0.8133230904302019 key: train_recall value: [0.84539474 0.88815789 0.83388158 0.85690789 0.85855263 0.8583196 0.8583196 0.84843493 0.8616145 0.8616145 ] mean value: 0.8571197866990374 key: test_accuracy value: [0.82222222 0.76296296 0.8 0.73333333 0.77037037 0.71111111 0.72592593 0.77037037 0.78518519 0.76296296] mean value: 0.7644444444444444 key: train_accuracy value: [0.82304527 0.84526749 0.81234568 0.82633745 0.83127572 0.83209877 0.82962963 0.82304527 0.82798354 0.82633745] mean value: 0.8277366255144033 key: test_roc_auc value: [0.82221247 0.76338894 0.80026339 0.73353819 0.77074188 0.71027217 0.72552678 0.7703029 0.7845698 0.76251097] mean value: 0.764332748024583 key: train_roc_auc value: [0.82302686 0.84523216 0.81232794 0.82631227 0.83125325 0.83212033 0.82965322 0.82306615 0.8280112 0.82636646] mean value: 0.8277369830052892 key: test_jcc value: [0.69620253 0.63218391 0.6746988 0.5862069 0.63953488 0.58947368 0.58888889 0.63095238 0.67045455 0.63636364] mean value: 0.6344960151014902 key: train_jcc value: [0.70507545 0.74175824 0.68979592 0.71174863 0.71801926 0.71862069 0.71565934 0.70547945 0.71448087 0.71253406] mean value: 0.7133171913674766 MCC on Blind test: 0.42 MCC on Training: 0.53 Running classifier: 11 Model_name: Logistic Regression Model func: LogisticRegression(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegression(random_state=42))]) key: fit_time value: [0.05070019 0.04871011 0.10884142 0.05884218 0.05360532 0.05006576 0.04953194 0.04862118 0.04995775 0.06115365] mean value: 0.05800294876098633 key: score_time value: [0.01259685 0.01287413 0.01674056 0.01268911 0.01266384 0.01266408 0.01290464 0.01251054 0.01248455 0.0126698 ] mean value: 0.01307981014251709 key: test_mcc value: [0.61882494 0.4830632 0.48156277 0.42230026 0.52589991 0.44126823 0.40737489 0.46769271 0.57168337 0.52661808] mean value: 0.49462883656514894 key: train_mcc value: [0.56385291 0.58189474 0.55068289 0.5769632 0.57369631 0.6068911 0.5851892 0.59177358 0.54245382 0.55396452] mean value: 0.5727362276845336 key: test_fscore value: [0.79365079 0.74820144 0.74074074 0.71111111 0.76119403 0.73972603 0.70588235 0.72727273 0.79432624 0.77142857] mean value: 0.7493534034376801 key: train_fscore value: [0.78044739 0.79146141 0.77381939 0.78812861 0.78822567 0.80616383 0.79276316 0.79605263 0.76910299 0.77510373] mean value: 0.7861268812623662 key: test_precision value: [0.84745763 0.72222222 0.73529412 0.70588235 0.76119403 0.69230769 0.70588235 0.75 0.76712329 0.75 ] mean value: 0.7437363682699949 key: train_precision value: [0.78631052 0.79016393 0.77963272 0.79008264 0.78373984 0.79392971 0.79146141 0.79474548 0.77554439 0.78093645] mean value: 0.7866547107654923 key: test_recall value: [0.74626866 0.7761194 0.74626866 0.71641791 0.76119403 0.79411765 0.70588235 0.70588235 0.82352941 0.79411765] mean value: 0.7569798068481124 key: train_recall value: [0.77467105 0.79276316 0.76809211 0.78618421 0.79276316 0.81878089 0.79406919 0.79736409 0.76276771 0.7693575 ] mean value: 0.7856813058180873 key: test_accuracy value: [0.80740741 0.74074074 0.74074074 0.71111111 0.76296296 0.71851852 0.7037037 0.73333333 0.78518519 0.76296296] mean value: 0.7466666666666667 key: train_accuracy value: [0.781893 0.7909465 0.77530864 0.78847737 0.78683128 0.80329218 0.79259259 0.79588477 0.77119342 0.77695473] mean value: 0.7863374485596708 key: test_roc_auc value: [0.80695786 0.74100088 0.74078139 0.71115013 0.76294996 0.71795435 0.70368745 0.73353819 0.78489903 0.76273047] mean value: 0.7465649692712906 key: train_roc_auc value: [0.78189895 0.79094501 0.77531459 0.78847926 0.78682639 0.80330492 0.79259381 0.79588599 0.77118649 0.77694848] mean value: 0.7863383876701638 key: test_jcc value: [0.65789474 0.59770115 0.58823529 0.55172414 0.61445783 0.58695652 0.54545455 0.57142857 0.65882353 0.62790698] mean value: 0.6000583294419574 key: train_jcc value: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:444: ConvergenceWarning: lbfgs failed to converge (status=1): STOP: TOTAL NO. of ITERATIONS REACHED LIMIT. Increase the number of iterations (max_iter) or scale the data as shown in: https://scikit-learn.org/stable/modules/preprocessing.html Please also refer to the documentation for alternative solver options: https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression n_iter_i = _check_optimize_result( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/neural_network/_multilayer_perceptron.py:702: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet. warnings.warn( [0.63994565 0.6548913 0.63108108 0.65034014 0.65047233 0.67527174 0.65667575 0.66120219 0.62483131 0.63279133] mean value: 0.6477502819536802 MCC on Blind test: 0.38 MCC on Training: 0.49 Running classifier: 12 Model_name: Logistic RegressionCV Model func: LogisticRegressionCV(cv=3, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', LogisticRegressionCV(cv=3, random_state=42))]) key: fit_time value: [0.66414499 0.73158431 0.6744833 0.66237402 0.79673743 0.67436934 0.6675303 0.78624201 0.69527698 0.68888974] mean value: 0.7041632413864136 key: score_time value: [0.01276112 0.01272035 0.01283574 0.01286602 0.01260161 0.01271081 0.01254654 0.02057171 0.01352143 0.01341581] mean value: 0.01365511417388916 key: test_mcc value: [0.67405619 0.57215273 0.64571485 0.4830632 0.57105218 0.45728771 0.52922033 0.55631459 0.55744809 0.60292672] mean value: 0.5649236584478504 key: train_mcc value: [0.66060386 0.67237193 0.66452172 0.6632942 0.67079899 0.65791013 0.67844418 0.69550836 0.63695414 0.65720404] mean value: 0.6657611547727773 key: test_fscore value: [0.8358209 0.79136691 0.82608696 0.74820144 0.78832117 0.74829932 0.77777778 0.78571429 0.78873239 0.81118881] mean value: 0.7901509954026043 key: train_fscore value: [0.83505976 0.84101749 0.83768804 0.83534137 0.84034948 0.83116883 0.84294872 0.85191083 0.82220434 0.83293365] mean value: 0.837062251709925 key: test_precision value: [0.8358209 0.76388889 0.8028169 0.72222222 0.77142857 0.69620253 0.73684211 0.76388889 0.75675676 0.77333333] mean value: 0.7623201095358227 key: train_precision value: [0.80989181 0.81384615 0.80763359 0.81632653 0.81259601 0.8192 0.82059282 0.82434515 0.80345912 0.80900621] mean value: 0.8136897387504238 key: test_recall value: [0.8358209 0.82089552 0.85074627 0.7761194 0.80597015 0.80882353 0.82352941 0.80882353 0.82352941 0.85294118] mean value: 0.8207199297629499 key: train_recall value: [0.86184211 0.87006579 0.87006579 0.85526316 0.87006579 0.84349259 0.86655684 0.88138386 0.84184514 0.8583196 ] mean value: 0.8618900654643197 key: test_accuracy value: [0.83703704 0.78518519 0.82222222 0.74074074 0.78518519 0.72592593 0.76296296 0.77777778 0.77777778 0.8 ] mean value: 0.7814814814814814 key: train_accuracy value: [0.82962963 0.83539095 0.83127572 0.83127572 0.8345679 0.82880658 0.83868313 0.84691358 0.818107 0.82798354] mean value: 0.8322633744855967 key: test_roc_auc value: [0.83702809 0.78544776 0.82243196 0.74100088 0.78533802 0.72530729 0.76251097 0.77754609 0.77743635 0.79960492] mean value: 0.7813652326602282 key: train_roc_auc value: [0.8296031 0.83536238 0.83124377 0.83125596 0.83453866 0.82881866 0.83870605 0.84694193 0.81812652 0.82800849] mean value: 0.8322605512442556 key: test_jcc value: [0.71794872 0.6547619 0.7037037 0.59770115 0.65060241 0.59782609 0.63636364 0.64705882 0.65116279 0.68235294] mean value: 0.6539482164201884 key: train_jcc value: [0.71682627 0.72565158 0.72070845 0.71724138 0.72465753 0.71111111 0.72853186 0.74202497 0.69808743 0.71369863] mean value: 0.7198539197540412 MCC on Blind test: 0.46 MCC on Training: 0.56 Running classifier: 13 Model_name: MLP Model func: MLPClassifier(max_iter=500, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MLPClassifier(max_iter=500, random_state=42))]) key: fit_time value: [3.86589217 4.44094658 3.51118159 2.96147895 4.82316089 5.23659372 3.66530776 2.94406652 4.29153156 5.67280817] mean value: 4.141296792030334 key: score_time value: [0.01290607 0.01291323 0.0128336 0.01294899 0.0129745 0.01315498 0.01629114 0.01302433 0.0131321 0.01308942] mean value: 0.01332683563232422 key: test_mcc value: [0.80825847 0.77558491 0.78002828 0.68965532 0.83156432 0.73624888 0.84144718 0.74895689 0.88490475 0.81163213] mean value: 0.7908281133670491 key: train_mcc value: [0.94254046 0.93261177 0.92210952 0.86364046 0.96091475 0.95492594 0.87318231 0.8864796 0.93910176 0.94378272] mean value: 0.9219289300808361 key: test_fscore value: [0.90510949 0.89041096 0.89208633 0.84671533 0.91666667 0.87179487 0.92307692 0.87218045 0.94366197 0.90909091] mean value: 0.8970793900945784 key: train_fscore value: [0.97147514 0.96653061 0.96147673 0.93089092 0.98055105 0.97745572 0.93740219 0.94348894 0.9696 0.97193264] mean value: 0.9610803947435673 key: test_precision value: [0.88571429 0.82278481 0.86111111 0.82857143 0.85714286 0.77272727 0.88 0.89230769 0.90540541 0.86666667] mean value: 0.8572431529773302 key: train_precision value: [0.9628433 0.95948136 0.93887147 0.94266442 0.96645367 0.95590551 0.89269747 0.93811075 0.94245723 0.946875 ] mean value: 0.9446360181943432 key: test_recall value: [0.92537313 0.97014925 0.92537313 0.86567164 0.98507463 1. 0.97058824 0.85294118 0.98529412 0.95588235] mean value: 0.9436347673397718 key: train_recall value: [0.98026316 0.97368421 0.98519737 0.91940789 0.99506579 1. 0.98682043 0.94892916 0.99835255 0.99835255] mean value: 0.9786073116275036 key: test_accuracy value: [0.9037037 0.88148148 0.88888889 0.84444444 0.91111111 0.85185185 0.91851852 0.87407407 0.94074074 0.9037037 ] mean value: 0.8918518518518519 key: train_accuracy value: [0.97119342 0.96625514 0.96049383 0.93168724 0.98024691 0.97695473 0.93415638 0.94320988 0.96872428 0.97119342] mean value: 0.9604115226337449 key: test_roc_auc value: [0.90386304 0.88213345 0.88915716 0.84460053 0.91165496 0.85074627 0.91812994 0.87423178 0.94040825 0.90331431] mean value: 0.8918239683933276 key: train_roc_auc value: [0.97118594 0.96624902 0.96047348 0.93169736 0.98023471 0.97697368 0.93419969 0.94321458 0.96874865 0.97121575] mean value: 0.960419285962022 key: test_jcc value: [0.82666667 0.80246914 0.80519481 0.73417722 0.84615385 0.77272727 0.85714286 0.77333333 0.89333333 0.83333333] mean value: 0.8144531798877791 key: train_jcc value: [0.94453249 0.93522907 0.92581144 0.87071651 0.9618442 0.95590551 0.88217968 0.89302326 0.94099379 0.94539782] mean value: 0.9255633749841335 MCC on Blind test: 0.06 MCC on Training: 0.79 Running classifier: 14 Model_name: Multinomial Model func: MultinomialNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', MultinomialNB())]) key: fit_time value: [0.01712108 0.01721001 0.01711345 0.0169363 0.01708078 0.01710081 0.01708746 0.0171113 0.01711297 0.01734519] mean value: 0.01712193489074707 key: score_time value: [0.01254606 0.01254249 0.01247334 0.01248193 0.01249361 0.01250744 0.01253629 0.01249146 0.01253295 0.01247501] mean value: 0.012508058547973632 key: test_mcc value: [0.34812219 0.4669594 0.42217345 0.42217345 0.29027469 0.3512815 0.31848112 0.48859458 0.45336669 0.49659716] mean value: 0.40580242304862113 key: train_mcc value: [0.40968435 0.42609322 0.45878163 0.41957506 0.3819318 0.4930237 0.37081252 0.44099433 0.36045672 0.36487283] mean value: 0.41262261512339415 key: test_fscore value: [0.66666667 0.73529412 0.70676692 0.70676692 0.61904762 0.69863014 0.66176471 0.72 0.71755725 0.74626866] mean value: 0.697876298944128 key: train_fscore value: [0.69653423 0.70548523 0.72422464 0.70160609 0.68027211 0.74503311 0.67067928 0.71186441 0.6683717 0.67563025] mean value: 0.6979701051581512 key: test_precision value: [0.67692308 0.72463768 0.71212121 0.71212121 0.66101695 0.65384615 0.66176471 0.78947368 0.74603175 0.75757576] mean value: 0.7095512179024002 key: train_precision value: [0.71652174 0.72443674 0.73846154 0.72173913 0.70422535 0.74875208 0.70143885 0.73298429 0.69257951 0.68953688] mean value: 0.7170676107405142 key: test_recall value: [0.65671642 0.74626866 0.70149254 0.70149254 0.58208955 0.75 0.66176471 0.66176471 0.69117647 0.73529412] mean value: 0.6888059701492538 key: train_recall value: [0.67763158 0.6875 0.71052632 0.68256579 0.65789474 0.74135091 0.64250412 0.69192751 0.64579901 0.66227348] mean value: 0.6799973445764328 key: test_accuracy value: [0.67407407 0.73333333 0.71111111 0.71111111 0.64444444 0.67407407 0.65925926 0.74074074 0.72592593 0.74814815] mean value: 0.7022222222222223 key: train_accuracy value: [0.70452675 0.7127572 0.72921811 0.70946502 0.69053498 0.74650206 0.68477366 0.72016461 0.67983539 0.68230453] mean value: 0.7060082304526749 key: test_roc_auc value: [0.67394644 0.73342845 0.71104039 0.71104039 0.64398595 0.67350746 0.65924056 0.74133011 0.72618525 0.74824407] mean value: 0.7021949078138718 key: train_roc_auc value: [0.7045489 0.71277801 0.7292335 0.70948718 0.69056187 0.74649782 0.6847389 0.72014139 0.6798074 0.68228805] mean value: 0.7060083022630712 key: test_jcc value: [0.5 0.58139535 0.54651163 0.54651163 0.44827586 0.53684211 0.49450549 0.5625 0.55952381 0.5952381 ] mean value: 0.5371303971250685 key: train_jcc value: [0.53437095 0.54498044 0.56767411 0.54036458 0.51546392 0.59366755 0.50452781 0.55263158 0.50192061 0.51015228] mean value: 0.536575384167109 MCC on Blind test: 0.1 MCC on Training: 0.41 Running classifier: 15 Model_name: Naive Bayes Model func: BernoulliNB() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', BernoulliNB())]) key: fit_time value: [0.01787329 0.01777554 0.01789737 0.01774049 0.01796865 0.01965976 0.01788616 0.01794505 0.01792073 0.01785493] mean value: 0.018052196502685545 key: score_time value: [0.01275849 0.01269507 0.01268554 0.0127008 0.01274323 0.01271009 0.01271987 0.01268101 0.01271868 0.01276541] mean value: 0.012717819213867188 key: test_mcc value: [0.36303775 0.49692157 0.33695759 0.28884987 0.24489353 0.26383437 0.45385143 0.28884987 0.33340688 0.27722118] mean value: 0.3347824054689408 key: train_mcc value: [0.3679421 0.38474728 0.37947641 0.38442672 0.38608541 0.40110358 0.3580355 0.37130352 0.37466492 0.34323909] mean value: 0.37510245416983745 key: test_fscore value: [0.68148148 0.73846154 0.68531469 0.64179104 0.62773723 0.66666667 0.74125874 0.64705882 0.67625899 0.61417323] mean value: 0.6720202428918229 key: train_fscore value: [0.6867863 0.69935691 0.69274654 0.69543974 0.69650122 0.70550162 0.67980296 0.68892508 0.69155844 0.66887967] mean value: 0.6905498471098706 key: test_precision value: [0.67647059 0.76190476 0.64473684 0.64179104 0.61428571 0.6097561 0.70666667 0.64705882 0.66197183 0.66101695] mean value: 0.6625659319202664 key: train_precision value: [0.68122977 0.68396226 0.68659128 0.68870968 0.68921095 0.69316375 0.67757774 0.68115942 0.6816 0.67391304] mean value: 0.6837117898528542 key: test_recall value: [0.68656716 0.71641791 0.73134328 0.64179104 0.64179104 0.73529412 0.77941176 0.64705882 0.69117647 0.57352941] mean value: 0.6844381035996489 key: train_recall value: [0.69243421 0.71546053 0.69901316 0.70230263 0.70394737 0.71828666 0.68204283 0.69686985 0.70181219 0.66392092] mean value: 0.6976090349432065 key: test_accuracy value: [0.68148148 0.74814815 0.66666667 0.64444444 0.62222222 0.62962963 0.72592593 0.64444444 0.66666667 0.63703704] mean value: 0.6666666666666667 key: train_accuracy value: [0.68395062 0.69218107 0.68971193 0.69218107 0.69300412 0.70041152 0.67901235 0.68559671 0.6872428 0.67160494] mean value: 0.6874897119341563 key: test_roc_auc value: [0.68151888 0.74791484 0.66714223 0.64442493 0.62236611 0.62884109 0.72552678 0.64442493 0.66648376 0.63751097] mean value: 0.6666154521510098 key: train_roc_auc value: [0.68394363 0.69216189 0.68970427 0.69217273 0.6929951 0.70042622 0.67901484 0.68560598 0.68725478 0.67159862] mean value: 0.6874878067285183 key: test_jcc value: [0.51685393 0.58536585 0.5212766 0.47252747 0.45744681 0.5 0.58888889 0.47826087 0.51086957 0.44318182] mean value: 0.5074671804878914 key: train_jcc value: [0.52298137 0.53770087 0.52992519 0.53308365 0.53433208 0.545 0.51492537 0.52546584 0.52853598 0.50249377] mean value: 0.5274444106473448 MCC on Blind test: -0.0 MCC on Training: 0.33 Running classifier: 16 Model_name: Passive Aggresive Model func: PassiveAggressiveClassifier(n_jobs=12, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/discriminant_analysis.py:887: UserWarning: Variables are collinear warnings.warn("Variables are collinear") Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', PassiveAggressiveClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.0307622 0.02361107 0.03355265 0.04072237 0.0309329 0.033077 0.02900124 0.04783773 0.03199911 0.03748035] mean value: 0.03389766216278076 key: score_time value: [0.01246953 0.01237917 0.02084422 0.01581001 0.02210999 0.01238489 0.01237965 0.01237965 0.01235223 0.012393 ] mean value: 0.014550232887268066 key: test_mcc value: [0.33406727 0.36303775 0.17819358 0.44294438 0.37973264 0.26252168 0.47957456 0.48445944 0.55039428 0.3975592 ] mean value: 0.3872484767529443 key: train_mcc value: [0.40552443 0.47020406 0.25640905 0.50360782 0.40062868 0.27458994 0.4202759 0.57140945 0.54327388 0.42093732] mean value: 0.4266860539665741 key: test_fscore value: [0.55238095 0.68148148 0.25 0.65486726 0.55445545 0.38202247 0.76744186 0.72868217 0.79194631 0.57692308] mean value: 0.594020102460993 key: train_fscore value: [0.60699588 0.74586939 0.27170868 0.67472306 0.52716763 0.37340153 0.74530136 0.77824979 0.78538813 0.56375839] mean value: 0.6072563854135835 key: test_precision value: [0.76315789 0.67647059 0.76923077 0.80434783 0.82352941 0.80952381 0.63461538 0.7704918 0.72839506 0.83333333] mean value: 0.7613095882534179 key: train_precision value: [0.81043956 0.71493213 0.91509434 0.87012987 0.88715953 0.83428571 0.61431624 0.80350877 0.72984441 0.87804878] mean value: 0.8057759348995148 key: test_recall value: [0.43283582 0.68656716 0.14925373 0.55223881 0.41791045 0.25 0.97058824 0.69117647 0.86764706 0.44117647] mean value: 0.5459394205443371 key: train_recall value: [0.48519737 0.77960526 0.15953947 0.55098684 0.375 0.24052718 0.94728171 0.75453048 0.85008237 0.41515651] mean value: 0.5557907201075175 key: test_accuracy value: [0.65185185 0.68148148 0.55555556 0.71111111 0.66666667 0.59259259 0.7037037 0.74074074 0.77037037 0.67407407] mean value: 0.6748148148148149 key: train_accuracy value: [0.68559671 0.73415638 0.57201646 0.73415638 0.66337449 0.59670782 0.67654321 0.78518519 0.76790123 0.67901235] mean value: 0.6894650205761318 key: test_roc_auc value: [0.65024144 0.68151888 0.55256804 0.70994293 0.66483758 0.59514925 0.70171203 0.74111062 0.76964442 0.67581212] mean value: 0.6742537313432837 key: train_roc_auc value: [0.68576178 0.73411894 0.57235623 0.73430726 0.66361203 0.59641491 0.67676586 0.78515998 0.76796882 0.67879536] mean value: 0.6895261152778982 key: test_jcc value: [0.38157895 0.51685393 0.14285714 0.48684211 0.38356164 0.23611111 0.62264151 0.57317073 0.65555556 0.40540541] mean value: 0.4404578085121959 key: train_jcc value: [0.43574594 0.59473024 0.15721232 0.50911854 0.35792779 0.22955975 0.59400826 0.63699583 0.64661654 0.39252336] mean value: 0.4554438567822444 MCC on Blind test: 0.44 MCC on Training: 0.39 Running classifier: 17 Model_name: QDA Model func: QuadraticDiscriminantAnalysis() Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', QuadraticDiscriminantAnalysis())]) key: fit_time value: [0.04385471 0.0443604 0.04368663 0.04365468 0.04348874 0.04390049 0.0437932 0.04312563 0.04558229 0.04458928] mean value: 0.04400360584259033 key: score_time value: [0.01336622 0.01340175 0.01345158 0.01344323 0.0133698 0.01348233 0.01342535 0.01339364 0.01399708 0.01336908] mean value: 0.013470005989074708 key: test_mcc value: [0.56459864 0.5419349 0.5419349 0.47123334 0.61165162 0.50286087 0.47881205 0.52694237 0.56153061 0.48027581] mean value: 0.5281775120411671 key: train_mcc value: [0.51837166 0.56419053 0.54768476 0.51562549 0.58477613 0.55043851 0.52091688 0.50344058 0.54678396 0.50984821] mean value: 0.5362076711083291 key: test_fscore value: [0.79289941 0.78362573 0.78362573 0.75862069 0.81481481 0.77456647 0.76571429 0.7816092 0.79532164 0.76404494] mean value: 0.7814842911094465 key: train_fscore value: [0.77650064 0.79477124 0.78807518 0.77571885 0.80426099 0.78954248 0.77784932 0.77030457 0.78728923 0.77275621] mean value: 0.783706871089244 key: test_precision value: [0.65686275 0.64423077 0.64423077 0.61682243 0.69473684 0.63809524 0.62616822 0.64150943 0.66019417 0.61818182] mean value: 0.644103244486705 key: train_precision value: [0.63465553 0.65943601 0.65026738 0.63427377 0.67561521 0.65438787 0.6384778 0.62641899 0.64919786 0.62966805] mean value: 0.6452398471774945 key: test_recall value: [1. 1. 1. 0.98507463 0.98507463 0.98529412 0.98529412 1. 1. 1. ] mean value: 0.9940737489025461 key: train_recall value: [1. 1. 1. 0.99835526 0.99342105 0.99505766 0.99505766 1. 1. 1. ] mean value: 0.9981891637041533 key: test_accuracy value: [0.74074074 0.72592593 0.72592593 0.68888889 0.77777778 0.71111111 0.6962963 0.71851852 0.74074074 0.68888889] mean value: 0.7214814814814815 key: train_accuracy value: [0.71193416 0.74156379 0.7308642 0.71111111 0.75802469 0.73497942 0.71604938 0.70205761 0.73004115 0.70617284] mean value: 0.7242798353909464 key: test_roc_auc value: [0.74264706 0.72794118 0.72794118 0.69106673 0.77930202 0.70906497 0.6941396 0.71641791 0.73880597 0.68656716] mean value: 0.7213893766461809 key: train_roc_auc value: [0.71169687 0.74135091 0.7306425 0.7108745 0.75783079 0.7351933 0.71627883 0.70230263 0.73026316 0.70641447] mean value: 0.7242847968871933 key: test_jcc value: [0.65686275 0.64423077 0.64423077 0.61111111 0.6875 0.63207547 0.62037037 0.64150943 0.66019417 0.61818182] mean value: 0.6416266663640535 key: train_jcc value: [0.63465553 0.65943601 0.65026738 0.63361169 0.67260579 0.65226782 0.63645943 0.62641899 0.64919786 0.62966805] mean value: 0.6444588551341541 MCC on Blind test: 0.13 MCC on Training: 0.53 Running classifier: 18 Model_name: Random Forest Model func: RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42) Running model pipeline: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/sklearn/ensemble/_forest.py:427: FutureWarning: `max_features='auto'` has been deprecated in 1.1 and will be removed in 1.3. To keep the past behaviour, explicitly set `max_features='sqrt'` or remove this parameter as it is also the default value for RandomForestClassifiers and ExtraTreesClassifiers. warn( Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(n_estimators=1000, n_jobs=12, random_state=42))]) key: fit_time value: [0.91356826 0.92199731 0.92317629 0.90775418 0.89899611 0.92891574 0.96325469 0.91327953 0.95096183 0.94270706] mean value: 0.9264611005783081 key: score_time value: [0.20586205 0.17190361 0.16357374 0.19772196 0.15140247 0.2689209 0.20936322 0.17950177 0.22979927 0.18777323] mean value: 0.19658222198486328 key: test_mcc value: [0.98529412 1. 0.9565124 0.9565124 0.90122245 0.9423692 0.94113792 0.98529091 0.9707887 0.95648435] mean value: 0.9595612438831248 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.99259259 1. 0.97810219 0.97810219 0.95035461 0.97142857 0.97101449 0.99270073 0.98550725 0.97841727] mean value: 0.9798219888756778 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.98529412 1. 0.95714286 0.95714286 0.90540541 0.94444444 0.95714286 0.98550725 0.97142857 0.95774648] mean value: 0.9621254835604104 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 0.98529412 1. 1. 1. ] mean value: 0.9985294117647058 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.99259259 1. 0.97777778 0.97777778 0.94814815 0.97037037 0.97037037 0.99259259 0.98518519 0.97777778] mean value: 0.9792592592592593 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.99264706 1. 0.97794118 0.97794118 0.94852941 0.97014925 0.970259 0.99253731 0.98507463 0.97761194] mean value: 0.9792690956979806 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.98529412 1. 0.95714286 0.95714286 0.90540541 0.94444444 0.94366197 0.98550725 0.97142857 0.95774648] mean value: 0.9607773950292232 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.31 MCC on Training: 0.96 Running classifier: 19 Model_name: Random Forest2 Model func: RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea...age_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RandomForestClassifier(max_features='auto', min_samples_leaf=5, n_estimators=1000, n_jobs=12, oob_score=True, random_state=42))]) key: fit_time value: [1.34544349 1.33649063 1.33248019 1.39131165 1.30751896 1.29677629 1.25967312 1.34208298 1.30583954 1.39401507] mean value: 1.331163191795349 key: score_time value: [0.27655005 0.29448676 0.29461026 0.20789409 0.2074821 0.23678875 0.26180673 0.29158831 0.25240517 0.22921467] mean value: 0.25528268814086913 key: test_mcc value: [0.91267269 0.95566286 0.91152582 0.80825847 0.87145367 0.87126224 0.86673987 0.92681399 0.92681399 0.88147498] mean value: 0.8932678571292083 key: train_mcc value: [0.98026804 0.98525044 0.97695995 0.97695995 0.98356032 0.98192637 0.97697602 0.97697602 0.98525072 0.98685273] mean value: 0.9810980565591365 key: test_fscore value: [0.95384615 0.97777778 0.95588235 0.90510949 0.93617021 0.93706294 0.93430657 0.96402878 0.96402878 0.94117647] mean value: 0.9469389517333232 key: train_fscore value: [0.99016393 0.99264105 0.98850575 0.98850575 0.99180328 0.99097621 0.98850575 0.98850575 0.99262899 0.99343186] mean value: 0.9905668306365223 key: test_precision value: [0.98412698 0.97058824 0.94202899 0.88571429 0.89189189 0.89333333 0.92753623 0.94366197 0.94366197 0.94117647] mean value: 0.9323720362002124 key: train_precision value: [0.9869281 0.98699187 0.98688525 0.98688525 0.98856209 0.9869281 0.98527005 0.98527005 0.98697068 0.99018003] mean value: 0.9870871477347558 key: test_recall value: [0.92537313 0.98507463 0.97014925 0.92537313 0.98507463 0.98529412 0.94117647 0.98529412 0.98529412 0.94117647] mean value: 0.9629280070237052 key: train_recall value: [0.99342105 0.99835526 0.99013158 0.99013158 0.99506579 0.99505766 0.99176277 0.99176277 0.99835255 0.99670511] mean value: 0.9940746119830053 key: test_accuracy value: [0.95555556 0.97777778 0.95555556 0.9037037 0.93333333 0.93333333 0.93333333 0.96296296 0.96296296 0.94074074] mean value: 0.9459259259259261 key: train_accuracy value: [0.99012346 0.99259259 0.98847737 0.98847737 0.99176955 0.9909465 0.98847737 0.98847737 0.99259259 0.99341564] mean value: 0.9905349794238683 key: test_roc_auc value: [0.95533363 0.97783143 0.95566286 0.90386304 0.93371378 0.93294557 0.9332748 0.96279631 0.96279631 0.94073749] mean value: 0.9458955223880597 key: train_roc_auc value: [0.99012074 0.99258785 0.988476 0.988476 0.99176683 0.99094988 0.98848007 0.98848007 0.99259733 0.99341834] mean value: 0.9905353117142115 key: test_jcc value: [0.91176471 0.95652174 0.91549296 0.82666667 0.88 0.88157895 0.87671233 0.93055556 0.93055556 0.88888889] mean value: 0.8998737345561478 key: train_jcc value: [0.98051948 0.98538961 0.97727273 0.97727273 0.98373984 0.98211382 0.97727273 0.97727273 0.98536585 0.98694943] mean value: 0.9813168941232643 MCC on Blind test: 0.45 MCC on Training: 0.89 Running classifier: 20 Model_name: Ridge Classifier Model func: RidgeClassifier(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifier(random_state=42))]) key: fit_time value: [0.0428369 0.0508759 0.03146672 0.02000093 0.01999497 0.04633713 0.04248977 0.02001882 0.01976132 0.01996422] mean value: 0.031374669075012206 key: score_time value: [0.01944613 0.02632403 0.02968025 0.01271653 0.01280594 0.01945353 0.01967216 0.01258183 0.01275015 0.01260519] mean value: 0.01780357360839844 key: test_mcc value: [0.66087126 0.54399701 0.60184923 0.42279825 0.55802991 0.45535407 0.40860416 0.51119403 0.57168337 0.55631459] mean value: 0.5290695873630847 key: train_mcc value: [0.61316982 0.65351155 0.59508719 0.63316108 0.63160787 0.63499504 0.62152634 0.64124376 0.63641516 0.60828718] mean value: 0.6269004985384924 key: test_fscore value: [0.82170543 0.78014184 0.8057554 0.71532847 0.78571429 0.74482759 0.71830986 0.75555556 0.79432624 0.78571429] mean value: 0.7707378946645663 key: train_fscore value: [0.80658436 0.83079391 0.79869067 0.81914031 0.81877023 0.82038835 0.81239804 0.82189542 0.82017901 0.80523732] mean value: 0.8154077613236691 key: test_precision value: [0.85483871 0.74324324 0.77777778 0.7 0.75342466 0.7012987 0.68918919 0.76119403 0.76712329 0.76388889] mean value: 0.7511978485131445 key: train_precision value: [0.80724876 0.81064163 0.79478827 0.808 0.80573248 0.80604134 0.80452342 0.81523501 0.81028939 0.8 ] mean value: 0.8062500307153451 key: test_recall value: [0.79104478 0.82089552 0.8358209 0.73134328 0.82089552 0.79411765 0.75 0.75 0.82352941 0.80882353] mean value: 0.7926470588235294 key: train_recall value: [0.80592105 0.85197368 0.80263158 0.83059211 0.83223684 0.83525535 0.82042834 0.82866557 0.83031301 0.81054366] mean value: 0.8248561193965143 key: test_accuracy value: [0.82962963 0.77037037 0.8 0.71111111 0.77777778 0.72592593 0.7037037 0.75555556 0.78518519 0.77777778] mean value: 0.7637037037037037 key: train_accuracy value: [0.80658436 0.82633745 0.79753086 0.81646091 0.81563786 0.81728395 0.81069959 0.82057613 0.818107 0.80411523] mean value: 0.8133333333333332 key: test_roc_auc value: [0.82934592 0.77074188 0.80026339 0.71125988 0.77809482 0.72541703 0.70335821 0.75559701 0.78489903 0.77754609] mean value: 0.7636523266022828 key: train_roc_auc value: [0.80658491 0.82631633 0.79752666 0.81644927 0.81562419 0.81729873 0.81070759 0.82058278 0.81811703 0.80412051] mean value: 0.8133328004422093 key: test_jcc value: [0.69736842 0.63953488 0.6746988 0.55681818 0.64705882 0.59340659 0.56043956 0.60714286 0.65882353 0.64705882] mean value: 0.6282350469232065 key: train_jcc value: [0.67586207 0.71056241 0.66485014 0.69368132 0.69315068 0.69547325 0.68406593 0.69764216 0.69517241 0.6739726 ] mean value: 0.6884432988373393 MCC on Blind test: 0.55 MCC on Training: 0.53 Running classifier: 21 Model_name: Ridge ClassifierCV Model func: RidgeClassifierCV(cv=3) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', RidgeClassifierCV(cv=3))]) key: fit_time value: [0.08397198 0.12925959 0.14689136 0.14646697 0.13729692 0.20604038 0.15477061 0.07581687 0.11583567 0.15459728] mean value: 0.1350947618484497 key: score_time value: [0.01972461 0.02497101 0.03283596 0.01958466 0.01976132 0.02161384 0.02032948 0.01279283 0.01955605 0.02056265] mean value: 0.02117323875427246 key: test_mcc value: [0.66087126 0.54399701 0.63154574 0.4830632 0.52623492 0.45535407 0.4712638 0.51108673 0.57168337 0.54195407] mean value: 0.5397054166295101 key: train_mcc value: [0.61316982 0.65351155 0.6217257 0.65875403 0.63995354 0.63499504 0.65734084 0.65013738 0.63641516 0.65206659] mean value: 0.6418069644749018 key: test_fscore value: [0.82170543 0.78014184 0.82014388 0.74820144 0.76470588 0.74482759 0.75342466 0.75912409 0.79432624 0.78014184] mean value: 0.7766742892860936 key: train_fscore value: [0.80658436 0.83079391 0.81391586 0.83386581 0.82324455 0.82038835 0.83320032 0.82864039 0.82017901 0.83012821] mean value: 0.8240940759234732 key: test_precision value: [0.85483871 0.74324324 0.79166667 0.72222222 0.75362319 0.7012987 0.70512821 0.75362319 0.76712329 0.75342466] mean value: 0.754619207025353 key: train_precision value: [0.80724876 0.81064163 0.80095541 0.81055901 0.80824089 0.80604134 0.80804954 0.80974843 0.81028939 0.80811232] mean value: 0.8079886711952577 key: test_recall value: [0.79104478 0.82089552 0.85074627 0.7761194 0.7761194 0.79411765 0.80882353 0.76470588 0.82352941 0.80882353] mean value: 0.8014925373134328 key: train_recall value: [0.80592105 0.85197368 0.82730263 0.85855263 0.83881579 0.83525535 0.85996705 0.84843493 0.83031301 0.85337727] mean value: 0.840991340067632 key: test_accuracy value: [0.82962963 0.77037037 0.81481481 0.74074074 0.76296296 0.72592593 0.73333333 0.75555556 0.78518519 0.77037037] mean value: 0.7688888888888888 key: train_accuracy value: [0.80658436 0.82633745 0.81069959 0.82880658 0.81975309 0.81728395 0.82798354 0.82469136 0.818107 0.8255144 ] mean value: 0.820576131687243 key: test_roc_auc value: [0.82934592 0.77074188 0.81507902 0.74100088 0.7630597 0.72541703 0.73276997 0.75548727 0.78489903 0.77008341] mean value: 0.7687884108867428 key: train_roc_auc value: [0.80658491 0.82631633 0.81068591 0.82878208 0.81973738 0.81729873 0.82800984 0.82471088 0.81811703 0.82553732] mean value: 0.8205780423567155 key: test_jcc value: [0.69736842 0.63953488 0.69512195 0.59770115 0.61904762 0.59340659 0.6043956 0.61176471 0.65882353 0.63953488] mean value: 0.6356699341283227 key: train_jcc value: [0.67586207 0.71056241 0.68622101 0.71506849 0.69958848 0.69547325 0.71409029 0.70741758 0.69517241 0.70958904] mean value: 0.7009045038911454 MCC on Blind test: 0.51 MCC on Training: 0.54 Running classifier: 22 Model_name: SVC Model func: SVC(random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SVC(random_state=42))]) key: fit_time value: [0.11785197 0.09961343 0.1026473 0.10186505 0.10134912 0.09928107 0.10272717 0.09995794 0.1019392 0.10415483] mean value: 0.10313870906829833 key: score_time value: [0.03136253 0.03458595 0.03512001 0.0334394 0.0340507 0.03359127 0.03412914 0.03479695 0.03419399 0.03526258] mean value: 0.034053254127502444 key: test_mcc value: [0.65440924 0.57036225 0.6157076 0.48156277 0.51108673 0.44126823 0.66124225 0.54603502 0.65199431 0.58532848] mean value: 0.571899689543166 key: train_mcc value: [0.66513518 0.69226059 0.66962595 0.68910509 0.6742591 0.69966419 0.66969172 0.65161337 0.65875403 0.6632942 ] mean value: 0.6733403397891486 key: test_fscore value: [0.80327869 0.78195489 0.8 0.74074074 0.7518797 0.73972603 0.82442748 0.75590551 0.80952381 0.79710145] mean value: 0.7804538294654982 key: train_fscore value: [0.8277027 0.84507042 0.83151718 0.84263114 0.83527454 0.84530854 0.83095038 0.82154882 0.82342954 0.82700422] mean value: 0.833043749059366 key: test_precision value: [0.89090909 0.78787879 0.82539683 0.73529412 0.75757576 0.69230769 0.85714286 0.81355932 0.87931034 0.78571429] mean value: 0.802508908143384 key: train_precision value: [0.85069444 0.85141903 0.84786325 0.85328836 0.84511785 0.86805556 0.84879725 0.83993115 0.84938704 0.84775087] mean value: 0.850230479832559 key: test_recall value: [0.73134328 0.7761194 0.7761194 0.74626866 0.74626866 0.79411765 0.79411765 0.70588235 0.75 0.80882353] mean value: 0.7629060579455663 key: train_recall value: [0.80592105 0.83881579 0.81578947 0.83223684 0.82565789 0.82372323 0.81383855 0.80395387 0.79901153 0.80724876] mean value: 0.8166196999913293 key: test_accuracy value: [0.82222222 0.78518519 0.80740741 0.74074074 0.75555556 0.71851852 0.82962963 0.77037037 0.82222222 0.79259259] mean value: 0.7844444444444445 key: train_accuracy value: [0.83209877 0.84609053 0.8345679 0.84444444 0.83703704 0.84938272 0.8345679 0.8255144 0.82880658 0.83127572] mean value: 0.8363786008230452 key: test_roc_auc value: [0.82155399 0.78511853 0.80717735 0.74078139 0.75548727 0.71795435 0.82989464 0.77085162 0.82276119 0.79247147] mean value: 0.7844051799824407 key: train_roc_auc value: [0.83212033 0.84609653 0.83458337 0.8444545 0.83704641 0.84936161 0.83455085 0.82549667 0.82878208 0.83125596] mean value: 0.8363748320038151 key: test_jcc value: [0.67123288 0.64197531 0.66666667 0.58823529 0.60240964 0.58695652 0.7012987 0.60759494 0.68 0.6626506 ] mean value: 0.6409020546849166 key: train_jcc value: [0.70605187 0.73170732 0.71162123 0.72805755 0.71714286 0.73206442 0.71079137 0.69714286 0.6998557 0.70503597] mean value: 0.7139471152028267 MCC on Blind test: 0.31 MCC on Training: 0.57 Running classifier: 23 Model_name: Stochastic GDescent Model func: SGDClassifier(n_jobs=12, random_state=42) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_lineage_proportion', 'lineage_count_all', 'lineage_count_unique'], dtype='object', length=167)), ('cat', OneHotEncoder(), Index(['ss_class', 'aa_prop_change', 'electrostatics_change', 'polarity_change', 'water_change', 'active_site'], dtype='object'))])), ('model', SGDClassifier(n_jobs=12, random_state=42))]) key: fit_time value: [0.03628969 0.04989934 0.05494523 0.0397191 0.0614295 0.05327177 0.04425097 0.05923796 0.06238341 0.05577803] mean value: 0.051720499992370605 key: score_time value: [0.01134443 0.01258588 0.01276445 0.01296377 0.01275253 0.01283121 0.01279283 0.01284313 0.01312661 0.01308465] mean value: 0.012708950042724609 key: test_mcc value: [0.47941251 0.34657417 0.56439739 0.45244903 0.5737658 0.46094151 0.55637466 0.39223135 0.60639056 0.43735891] mean value: 0.48698958852304564 key: train_mcc value: [0.52026447 0.45242117 0.50737033 0.62160635 0.57204059 0.58712158 0.50171823 0.40742599 0.54646043 0.49980611] mean value: 0.5216235246271133 key: test_fscore value: [0.69491525 0.5 0.79738562 0.72992701 0.79432624 0.75949367 0.79754601 0.55445545 0.81818182 0.64285714] mean value: 0.7089088213325873 key: train_fscore value: [0.72118959 0.61341853 0.77352941 0.81331169 0.78723404 0.80714818 0.77380952 0.57453754 0.78962963 0.66666667] mean value: 0.7320474800696121 key: test_precision value: [0.80392157 0.82758621 0.70930233 0.71428571 0.75675676 0.66666667 0.68421053 0.84848485 0.73255814 0.81818182] mean value: 0.7561954571331876 key: train_precision value: [0.82905983 0.87009063 0.69946809 0.80288462 0.78338762 0.73641304 0.64640884 0.84615385 0.71736205 0.87433155] mean value: 0.7805560112115434 key: test_recall value: [0.6119403 0.35820896 0.91044776 0.74626866 0.8358209 0.88235294 0.95588235 0.41176471 0.92647059 0.52941176] mean value: 0.7168568920105355 key: train_recall value: [0.63815789 0.47368421 0.86513158 0.82401316 0.79111842 0.89291598 0.96375618 0.43492586 0.87808896 0.53871499] mean value: 0.7300507240093644 key: test_accuracy value: [0.73333333 0.64444444 0.77037037 0.72592593 0.78518519 0.71851852 0.75555556 0.66666667 0.79259259 0.7037037 ] mean value: 0.7296296296296296 key: train_accuracy value: [0.75308642 0.70123457 0.74650206 0.81069959 0.78600823 0.78683128 0.71851852 0.6781893 0.76625514 0.7308642 ] mean value: 0.7478189300411523 key: test_roc_auc value: [0.73244074 0.64233977 0.77140035 0.7260755 0.78555751 0.71729587 0.75406058 0.66856892 0.7915935 0.70500439] mean value: 0.7294337137840211 key: train_roc_auc value: [0.75318109 0.70142201 0.74640434 0.81068862 0.78600402 0.78691852 0.71872019 0.67798925 0.76634711 0.73070618] mean value: 0.7478381329662707 key: test_jcc value: [0.53246753 0.33333333 0.66304348 0.57471264 0.65882353 0.6122449 0.66326531 0.38356164 0.69230769 0.47368421] mean value: 0.5587444267902919 key: train_jcc value: [0.56395349 0.44239631 0.63069544 0.68536252 0.64912281 0.67665418 0.63106796 0.40305344 0.65238678 0.5 ] mean value: 0.5834692928956103 MCC on Blind test: 0.4 MCC on Training: 0.49 Running classifier: 24 Model_name: XGBoost Model func: /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/anaconda3/envs/UQ/lib/python3.9/site-packages/xgboost/compat.py:36: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead. from pandas import MultiIndex, Int64Index /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:463: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_CV['source_data'] = 'CV' /home/tanu/git/LSHTM_analysis/scripts/ml/ml_functions/MultClfs.py:490: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy scoresDF_BT['source_data'] = 'BT' XGBClassifier(base_score=None, booster=None, colsample_bylevel=None, colsample_bynode=None, colsample_bytree=None, enable_categorical=False, gamma=None, gpu_id=None, importance_type=None, interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0) Running model pipeline: Pipeline(steps=[('prep', ColumnTransformer(remainder='passthrough', transformers=[('num', MinMaxScaler(), Index(['consurf_score', 'snap2_score', 'provean_score', 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'contacts', 'electro_rr', 'electro_mm', ... 'ZHAC000105', 'ZHAC000106', 'rsa', 'kd_values', 'rd_values', 'maf', 'lineage_proportion', 'dist_linea... interaction_constraints=None, learning_rate=None, max_delta_step=None, max_depth=None, min_child_weight=None, missing=nan, monotone_constraints=None, n_estimators=100, n_jobs=12, num_parallel_tree=None, predictor=None, random_state=42, reg_alpha=None, reg_lambda=None, scale_pos_weight=None, subsample=None, tree_method=None, use_label_encoder=False, validate_parameters=None, verbosity=0))]) key: fit_time value: [0.22980762 0.21588039 0.20163631 0.2096765 0.24002385 0.2062161 0.20222735 0.22808814 0.38336062 0.20958424] mean value: 0.23265011310577394 key: score_time value: [0.01300263 0.0127511 0.01239276 0.01197529 0.01213312 0.01200509 0.01224422 0.01230168 0.0119915 0.0123322 ] mean value: 0.012312960624694825 key: test_mcc value: [0.98529412 0.98529412 0.9424184 0.92851083 0.86149266 0.92843493 0.95565315 0.98529091 0.9707887 0.98529091] mean value: 0.95284687234088 key: train_mcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_fscore value: [0.99259259 0.99259259 0.97101449 0.96402878 0.93055556 0.96453901 0.97810219 0.99270073 0.98550725 0.99270073] mean value: 0.9764333913576827 key: train_fscore value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_precision value: [0.98529412 0.98529412 0.94366197 0.93055556 0.87012987 0.93150685 0.97101449 0.98550725 0.97142857 0.98550725] mean value: 0.9559900039061416 key: train_precision value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_recall value: [1. 1. 1. 1. 1. 1. 0.98529412 1. 1. 1. ] mean value: 0.9985294117647058 key: train_recall value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_accuracy value: [0.99259259 0.99259259 0.97037037 0.96296296 0.92592593 0.96296296 0.97777778 0.99259259 0.98518519 0.99259259] mean value: 0.9755555555555555 key: train_accuracy value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_roc_auc value: [0.99264706 0.99264706 0.97058824 0.96323529 0.92647059 0.96268657 0.97772169 0.99253731 0.98507463 0.99253731] mean value: 0.9756145741878841 key: train_roc_auc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 key: test_jcc value: [0.98529412 0.98529412 0.94366197 0.93055556 0.87012987 0.93150685 0.95714286 0.98550725 0.97142857 0.98550725] mean value: 0.954602840345065 key: train_jcc value: [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.] mean value: 1.0 MCC on Blind test: 0.62 MCC on Training: 0.95 Extracting tts_split_name: sl Total cols in each df: CV df: 8 metaDF: 17 Adding column: Model_name Total cols in bts df: BT_df: 8 First proceeding to rowbind CV and BT dfs: Final output should have: 25 columns Combinig 2 using pd.concat by row ~ rowbind Checking Dims of df to combine: Dim of CV: (24, 8) Dim of BT: (24, 8) 8 Number of Common columns: 8 These are: ['source_data', 'Precision', 'JCC', 'MCC', 'Recall', 'Accuracy', 'F1', 'ROC_AUC'] Concatenating dfs with different resampling methods [WF]: Split type: sl No. of dfs combining: 2 PASS: 2 dfs successfully combined nrows in combined_df_wf: 48 ncols in combined_df_wf: 8 PASS: proceeding to merge metadata with CV and BT dfs Adding column: Model_name ========================================================= SUCCESS: Ran multiple classifiers ======================================================= Building estimator 8 of 8 for this parallel run (total 100)... 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Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 8 for this parallel run (total 100)... Building estimator 2 of 8 for this parallel run (total 100)... Building estimator 3 of 8 for this parallel run (total 100)... Building estimator 4 of 8 for this parallel run (total 100)... Building estimator 5 of 8 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 6 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 7 of 8 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)... Building estimator 8 of 8 for this parallel run (total 100)... Building estimator 1 of 9 for this parallel run (total 100)... Building estimator 2 of 9 for this parallel run (total 100)... Building estimator 3 of 9 for this parallel run (total 100)... Building estimator 4 of 9 for this parallel run (total 100)... Building estimator 5 of 9 for this parallel run (total 100)... Building estimator 6 of 9 for this parallel run (total 100)... Building estimator 7 of 9 for this parallel run (total 100)... Building estimator 8 of 9 for this parallel run (total 100)... Building estimator 9 of 9 for this parallel run (total 100)...